Repository 'sklearn_searchcv'
hg clone https://toolshed.g2.bx.psu.edu/repos/bgruening/sklearn_searchcv

Changeset 0:91bf3f0d7455 (2018-08-04)
Next changeset 1:f727f5ff7d60 (2018-08-04)
Commit message:
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 76583c1fcd9d06a4679cc46ffaee44117b9e22cd
added:
README.rst
main_macros.xml
search_model_validation.xml
test-data/RF01704.fasta
test-data/accuracy_score.txt
test-data/auc.txt
test-data/average_precision_score.txt
test-data/blobs.txt
test-data/brier_score_loss.txt
test-data/circles.txt
test-data/class.txt
test-data/classification_report.txt
test-data/cluster_result01.txt
test-data/cluster_result02.txt
test-data/cluster_result03.txt
test-data/cluster_result04.txt
test-data/cluster_result05.txt
test-data/cluster_result06.txt
test-data/cluster_result07.txt
test-data/cluster_result08.txt
test-data/cluster_result09.txt
test-data/cluster_result10.txt
test-data/cluster_result11.txt
test-data/cluster_result12
test-data/cluster_result12.txt
test-data/cluster_result13.txt
test-data/cluster_result14.txt
test-data/cluster_result15.txt
test-data/cluster_result16.txt
test-data/cluster_result17.txt
test-data/cluster_result18.txt
test-data/cluster_result19.txt
test-data/cluster_result20.txt
test-data/cluster_result21.txt
test-data/confusion_matrix.txt
test-data/converter_result01.json
test-data/converter_result02.json
test-data/csc_sparse1.mtx
test-data/csc_sparse2.mtx
test-data/csc_stack_result01.mtx
test-data/csr_sparse1.mtx
test-data/csr_sparse2.mtx
test-data/csr_stack_result01.mtx
test-data/empty_file.txt
test-data/f1_score.txt
test-data/fbeta_score.txt
test-data/feature_selection_result01
test-data/feature_selection_result02
test-data/feature_selection_result03
test-data/feature_selection_result04
test-data/feature_selection_result05
test-data/feature_selection_result06
test-data/feature_selection_result07
test-data/feature_selection_result08
test-data/feature_selection_result09
test-data/feature_selection_result10
test-data/feature_selection_result11
test-data/feature_selection_result12
test-data/friedman1.txt
test-data/friedman2.txt
test-data/friedman3.txt
test-data/gaus.txt
test-data/gbc_model01
test-data/gbc_result01
test-data/gbr_model01
test-data/gbr_prediction_result01.tabular
test-data/glm_model01
test-data/glm_model02
test-data/glm_model03
test-data/glm_model04
test-data/glm_model05
test-data/glm_model06
test-data/glm_model07
test-data/glm_model08
test-data/glm_result01
test-data/glm_result02
test-data/glm_result03
test-data/glm_result04
test-data/glm_result05
test-data/glm_result06
test-data/glm_result07
test-data/glm_result08
test-data/hamming_loss.txt
test-data/hastie.txt
test-data/hinge_loss.txt
test-data/jaccard_similarity_score.txt
test-data/lda_model01
test-data/lda_model02
test-data/lda_prediction_result01.tabular
test-data/lda_prediction_result02.tabular
test-data/log_loss.txt
test-data/matthews_corrcoef.txt
test-data/moons.txt
test-data/mv_result01.tabular
test-data/mv_result02.tabular
test-data/mv_result03.tabular
test-data/mv_result04.tabular
test-data/mv_result05.tabular
test-data/mv_result06.tabular
test-data/nn_model01.txt
test-data/nn_model02.txt
test-data/nn_model03.txt
test-data/nn_prediction_result01.tabular
test-data/nn_prediction_result02.tabular
test-data/nn_prediction_result03.tabular
test-data/numeric_values.tabular
test-data/pipeline01
test-data/pipeline02
test-data/pipeline03
test-data/pipeline04
test-data/pipeline05
test-data/pipeline06
test-data/pipeline07
test-data/pipeline08
test-data/precision_recall_curve.txt
test-data/precision_recall_fscore_support.txt
test-data/precision_score.txt
test-data/prp_model01
test-data/prp_model02
test-data/prp_model03
test-data/prp_model04
test-data/prp_model05
test-data/prp_model06
test-data/prp_model07
test-data/prp_model08
test-data/prp_model09
test-data/prp_result01
test-data/prp_result02
test-data/prp_result03
test-data/prp_result04
test-data/prp_result05
test-data/prp_result06
test-data/prp_result07
test-data/prp_result08
test-data/prp_result09
test-data/pw_metric01.tabular
test-data/pw_metric02.tabular
test-data/pw_metric03.tabular
test-data/qda_model01
test-data/qda_prediction_result01.tabular
test-data/recall_score.txt
test-data/regression.txt
test-data/regression_X.tabular
test-data/regression_metrics_result01
test-data/regression_metrics_result02
test-data/regression_metrics_result03
test-data/regression_metrics_result04
test-data/regression_metrics_result05
test-data/regression_metrics_result06
test-data/regression_test.tabular
test-data/regression_test_X.tabular
test-data/regression_test_y.tabular
test-data/regression_train.tabular
test-data/regression_y.tabular
test-data/rfc_model01
test-data/rfc_result01
test-data/rfc_result02
test-data/rfr_model01
test-data/rfr_result01
test-data/roc_auc_score.txt
test-data/roc_curve.txt
test-data/scurve.txt
test-data/searchCV01
test-data/sparse.mtx
test-data/sparse_u.txt
test-data/svc_model01.txt
test-data/svc_model02.txt
test-data/svc_model03.txt
test-data/svc_prediction_result01.tabular
test-data/svc_prediction_result02.tabular
test-data/svc_prediction_result03.tabular
test-data/swiss_r.txt
test-data/test.tabular
test-data/test2.tabular
test-data/test3.tabular
test-data/test_set.tabular
test-data/train.tabular
test-data/train_set.tabular
test-data/vectorizer_result01.mtx
test-data/vectorizer_result02.mtx
test-data/vectorizer_result03.mtx
test-data/vectorizer_result04.mtx
test-data/y.tabular
test-data/zero_one_loss.txt
b
diff -r 000000000000 -r 91bf3f0d7455 README.rst
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/README.rst Sat Aug 04 12:31:24 2018 -0400
b
@@ -0,0 +1,150 @@
+***************
+Galaxy wrapper for scikit-learn library
+***************
+
+Contents
+========
+- `What is scikit-learn?`_
+ - `Scikit-learn main package groups`_
+ - `Tools offered by this wrapper`_
+
+- `Machine learning workflows`_
+ - `Supervised learning workflows`_
+ - `Unsupervised learning workflows`_
+
+
+____________________________
+
+
+.. _What is scikit-learn?
+
+What is scikit-learn?
+===========================
+
+Scikit-learn is an open-source machine learning library for the Python programming language. It offers various algorithms for performing supervised and unsupervised learning as well as data preprocessing and transformation, model selection and evaluation, and dataset utilities. It is built upon SciPy (Scientific Python) library.
+
+Scikit-learn source code can be accessed at https://github.com/scikit-learn/scikit-learn.
+Detailed installation instructions can be found at http://scikit-learn.org/stable/install.html
+
+
+.. _Scikit-learn main package groups:
+
+======
+Scikit-learn main package groups
+======
+
+Scikit-learn provides the users with several main groups of related operations.
+These are:
+
+- Classification
+    - Identifying to which category an object belongs.
+- Regression
+    - Predicting a continuous-valued attribute associated with an object.
+- Clustering
+    - Automatic grouping of similar objects into sets.
+- Preprocessing
+    - Feature extraction and normalization.
+- Model selection and evaluation
+    - Comparing, validating and choosing parameters and models.
+- Dimensionality reduction
+    - Reducing the number of random variables to consider.
+
+Each group consists of a number of well-known algorithms from the category. For example, one can find hierarchical, spectral, kmeans, and other clustering methods in sklearn.cluster package.
+
+
+.. _Tools offered by this wrapper:
+
+===================
+Available tools in the current wrapper
+===================
+
+The current release of the wrapper offers a subset of the packages from scikit-learn library. You can find:
+
+- A subset of classification metric functions
+- Linear and quadratic discriminant classifiers
+- Random forest and Ada boost classifiers and regressors
+- All the clustering methods
+- All support vector machine classifiers
+- A subset of data preprocessing estimator classes
+- Pairwise metric measurement functions
+
+In addition, several tools for performing matrix operations, generating problem-specific datasets, and encoding text and extracting features have been prepared to help the user with more advanced operations.
+
+.. _Machine learning workflows:
+
+Machine learning workflows
+===============
+
+Machine learning is about processes. No matter what machine learning algorithm we use, we can apply typical workflows and dataflows to produce more robust models and better predictions.
+Here we discuss supervised and unsupervised learning workflows.
+
+.. _Supervised learning workflows:
+
+===================
+Supervised machine learning workflows
+===================
+
+**What is supervised learning?**
+
+In this machine learning task, given sample data which are labeled, the aim is to build a model which can predict the labels for new observations.
+In practice, there are five steps which we can go through to start from raw input data and end up getting reasonable predictions for new samples:
+
+1. Preprocess the data::
+
+    * Change the collected data into the proper format and datatype.
+    * Adjust the data quality by filling the missing values, performing
+    required scaling and normalizations, etc.
+    * Extract features which are the most meaningfull for the learning task.
+    * Split the ready dataset into training and test samples.
+
+2. Choose an algorithm::
+
+    * These factors help one to choose a learning algorithm:
+        - Nature of the data (e.g. linear vs. nonlinear data)
+        - Structure of the predicted output (e.g. binary vs. multilabel classification)
+        - Memory and time usage of the training
+        - Predictive accuracy on new data
+        - Interpretability of the predictions
+
+3. Choose a validation method
+
+ Every machine learning model should be evaluated before being put into practicical use.
+ There are numerous performance metrics to evaluate machine learning models.
+ For supervised learning, usually classification or regression metrics are used.
+
+ A validation method helps to evaluate the performance metrics of a trained model in order
+ to optimize its performance or ultimately switch to a more efficient model.
+ Cross-validation is a known validation method.
+
+4. Fit a model
+
+   Given the learning algorithm, validation method, and performance metric(s)
+   repeat the following steps::
+
+    * Train the model.
+    * Evaluate based on metrics.
+    * Optimize unitl satisfied.
+
+5. Use fitted model for prediction::
+
+ This is a final evaluation in which, the optimized model is used to make predictions
+ on unseen (here test) samples. After this, the model is put into production.
+
+.. _Unsupervised learning workflows:
+
+=======================
+Unsupervised machine learning workflows
+=======================
+
+**What is unsupervised learning?**
+
+Unlike supervised learning and more liklely in real life, here the initial data is not labeled.
+The task is to extract the structure from the data and group the samples based on their similarities.
+Clustering and dimensionality reduction are two famous examples of unsupervised learning tasks.
+
+In this case, the workflow is as follows::
+
+    * Preprocess the data (without splitting to train and test).
+    * Train a model.
+    * Evaluate and tune parameters.
+    * Analyse the model and test on real data.
b
diff -r 000000000000 -r 91bf3f0d7455 main_macros.xml
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/main_macros.xml Sat Aug 04 12:31:24 2018 -0400
[
b'@@ -0,0 +1,1515 @@\n+<macros>\n+  <token name="@VERSION@">0.9</token>\n+\n+  <token name="@COLUMNS_FUNCTION@">\n+def read_columns(f, c=None, c_option=\'by_index_number\', return_df=False, **args):\n+  data = pandas.read_csv(f, **args)\n+  if c_option == \'by_index_number\':\n+    cols = list(map(lambda x: x - 1, c))\n+    data = data.iloc[:,cols]\n+  if c_option == \'all_but_by_index_number\':\n+    cols = list(map(lambda x: x - 1, c))\n+    data.drop(data.columns[cols], axis=1, inplace=True)\n+  if c_option == \'by_header_name\':\n+    cols = [e.strip() for e in c.split(\',\')]\n+    data = data[cols]\n+  if c_option == \'all_but_by_header_name\':\n+    cols = [e.strip() for e in c.split(\',\')]\n+    data.drop(cols, axis=1, inplace=True)\n+  y = data.values\n+  if return_df:\n+    return y, data\n+  else:\n+    return y\n+  return y\n+  </token>\n+\n+## generate an instance for one of sklearn.feature_selection classes\n+  <token name="@FEATURE_SELECTOR_FUNCTION@">\n+def feature_selector(inputs):\n+  selector = inputs["selected_algorithm"]\n+  selector = getattr(sklearn.feature_selection, selector)\n+  options = inputs["options"]\n+\n+  if inputs[\'selected_algorithm\'] == \'SelectFromModel\':\n+    if not options[\'threshold\'] or options[\'threshold\'] == \'None\':\n+      options[\'threshold\'] = None\n+    if inputs[\'model_inputter\'][\'input_mode\'] == \'prefitted\':\n+      model_file = inputs[\'model_inputter\'][\'fitted_estimator\']\n+      with open(model_file, \'rb\') as model_handler:\n+        fitted_estimator = pickle.load(model_handler)\n+      new_selector = selector(fitted_estimator, prefit=True, **options)\n+    else:\n+      estimator_json = inputs[\'model_inputter\']["estimator_selector"]\n+      estimator = get_estimator(estimator_json)\n+      new_selector = selector(estimator, **options)\n+\n+  elif inputs[\'selected_algorithm\'] in [\'RFE\', \'RFECV\']:\n+    if \'scoring\' in options and (not options[\'scoring\'] or options[\'scoring\'] == \'None\'):\n+      options[\'scoring\'] = None\n+    estimator=get_estimator(inputs["estimator_selector"])\n+    new_selector = selector(estimator, **options)\n+\n+  elif inputs[\'selected_algorithm\'] == "VarianceThreshold":\n+    new_selector = selector(**options)\n+\n+  else:\n+    score_func = inputs["score_func"]\n+    score_func = getattr(sklearn.feature_selection, score_func)\n+    new_selector = selector(score_func, **options)\n+\n+  return new_selector\n+  </token>\n+\n+  <token name="@GET_X_y_FUNCTION@">\n+def get_X_y(params, file1, file2):\n+  input_type = params["selected_tasks"]["selected_algorithms"]["input_options"]["selected_input"]\n+  if input_type=="tabular":\n+    header = \'infer\' if params["selected_tasks"]["selected_algorithms"]["input_options"]["header1"] else None\n+    column_option = params["selected_tasks"]["selected_algorithms"]["input_options"]["column_selector_options_1"]["selected_column_selector_option"]\n+    if column_option in ["by_index_number", "all_but_by_index_number", "by_header_name", "all_but_by_header_name"]:\n+      c = params["selected_tasks"]["selected_algorithms"]["input_options"]["column_selector_options_1"]["col1"]\n+    else:\n+      c = None\n+    X = read_columns(\n+      file1,\n+      c = c,\n+      c_option = column_option,\n+      sep=\'\\t\',\n+      header=header,\n+      parse_dates=True\n+    )\n+  else:\n+    X = mmread(file1)\n+\n+  header = \'infer\' if params["selected_tasks"]["selected_algorithms"]["input_options"]["header2"] else None\n+  column_option = params["selected_tasks"]["selected_algorithms"]["input_options"]["column_selector_options_2"]["selected_column_selector_option2"]\n+  if column_option in ["by_index_number", "all_but_by_index_number", "by_header_name", "all_but_by_header_name"]:\n+    c = params["selected_tasks"]["selected_algorithms"]["input_options"]["column_selector_options_2"]["col2"]\n+  else:\n+    c = None\n+  y = read_columns(\n+    file2,\n+    c = c,\n+    c_option = column_option,\n+    sep=\'\\t\',\n+    header=header,\n+    parse_dates=True\n+  )\n+  y=y.ravel()\n+  return X, y\n+  </token>\n+\n+  <token name="@GET_SEARCH_PARAMS_FUNCTIO'..b'"Type in maxtrix decomposition parameters:"\n+              help="Parameters in dictionary without braces (\'{}\'), e.g., \'n_components\': 100, \'init\': \'random\'. No double quotes. Leave this box blank for class default."/>\n+      </when>\n+      <when value="PCA">\n+        <expand macro="estimator_params_text" label="Type in maxtrix decomposition parameters:"\n+              help="Parameters in dictionary without braces (\'{}\'), e.g., \'n_components\': 100, \'random_state\': 42. No double quotes. Leave this box blank for class default."/>\n+      </when>\n+      <when value="SparsePCA">\n+        <expand macro="estimator_params_text" label="Type in maxtrix decomposition parameters:"\n+              help="Parameters in dictionary without braces (\'{}\'), e.g., \'n_components\': 100, \'random_state\': 42. No double quotes. Leave this box blank for class default."/>\n+      </when>\n+      <when value="SparseCoder">\n+        <expand macro="estimator_params_text" label="Type in maxtrix decomposition parameters:"\n+              help="Parameters in dictionary without braces (\'{}\'), e.g., \'transform_algorithm\': \'omp\', \'transform_alpha\': 1.0. No double quotes. Leave this box blank for class default."/>\n+      </when>\n+      <when value="TruncatedSVD">\n+        <expand macro="estimator_params_text" label="Type in maxtrix decomposition parameters:"\n+              help="Parameters in dictionary without braces (\'{}\'), e.g., \'n_components\': 2, \'algorithm\': \'randomized\'. No double quotes. Leave this box blank for default estimator."/>\n+      </when>\n+    </conditional>\n+  </xml>\n+\n+  <xml name="FeatureAgglomeration">\n+    <conditional name="FeatureAgglomeration_selector">\n+      <param name="select_algorithm" type="select" label="Choose the algorithm:">\n+        <option value="FeatureAgglomeration" selected="true">FeatureAgglomeration</option>\n+      </param>\n+      <when value="FeatureAgglomeration">\n+        <expand macro="estimator_params_text" label="Type in parameters:"\n+              help="Parameters in dictionary without braces (\'{}\'), e.g., \'n_clusters\': 2, \'affinity\': \'euclidean\'. No double quotes. Leave this box blank for class default."/>\n+      </when>\n+    </conditional>\n+  </xml>\n+  <!-- Outputs -->\n+\n+  <xml name="output">\n+    <outputs>\n+      <data format="tabular" name="outfile_predict">\n+          <filter>selected_tasks[\'selected_task\'] == \'load\'</filter>\n+      </data>\n+      <data format="zip" name="outfile_fit">\n+          <filter>selected_tasks[\'selected_task\'] == \'train\'</filter>\n+      </data>\n+    </outputs>\n+  </xml>\n+\n+  <!--Citations-->\n+  <xml name="eden_citation">\n+    <citations>\n+        <citation type="doi">10.5281/zenodo.15094</citation>\n+    </citations>\n+  </xml>\n+\n+  <xml name="sklearn_citation">\n+    <citations>\n+        <citation type="bibtex">\n+            @article{scikit-learn,\n+             title={Scikit-learn: Machine Learning in {P}ython},\n+             author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n+                     and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n+                     and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n+                     Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n+             journal={Journal of Machine Learning Research},\n+             volume={12},\n+             pages={2825--2830},\n+             year={2011}\n+             url = {https://github.com/scikit-learn/scikit-learn}\n+            }\n+        </citation>\n+    </citations>\n+  </xml>\n+\n+  <xml name="scipy_citation">\n+    <citations>\n+        <citation type="bibtex">\n+          @Misc{,\n+          author =    {Eric Jones and Travis Oliphant and Pearu Peterson and others},\n+          title =     {{SciPy}: Open source scientific tools for {Python}},\n+          year =      {2001--},\n+          url = "http://www.scipy.org/",\n+          note = {[Online; accessed 2016-04-09]}\n+        }\n+        </citation>\n+    </citations>\n+  </xml>\n+\n+</macros>\n'
b
diff -r 000000000000 -r 91bf3f0d7455 search_model_validation.xml
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/search_model_validation.xml Sat Aug 04 12:31:24 2018 -0400
[
b'@@ -0,0 +1,401 @@\n+<tool id="sklearn_searchcv" name="Hyperparameter Search" version="@VERSION@">\n+    <description>using exhausitive or randomized search</description>\n+    <macros>\n+        <import>main_macros.xml</import>\n+    </macros>\n+    <expand macro="python_requirements">\n+        <requirement type="package" version="0.9.12">asteval</requirement>\n+    </expand>\n+    <expand macro="macro_stdio"/>\n+    <version_command>echo "@VERSION@"</version_command>\n+    <command>\n+        <![CDATA[\n+        python "$sklearn_search_model_validation_script" \'$inputs\'\n+        ]]>\n+    </command>\n+    <configfiles>\n+        <inputs name="inputs" />\n+        <configfile name="sklearn_search_model_validation_script">\n+            <![CDATA[\n+import sys\n+import json\n+import pandas\n+import pickle\n+import numpy as np\n+import xgboost\n+import scipy\n+from asteval import Interpreter, make_symbol_table\n+from sklearn import metrics, preprocessing, model_selection, ensemble\n+from sklearn.pipeline import Pipeline\n+\n+@COLUMNS_FUNCTION@\n+@GET_ESTIMATOR_FUNCTION@\n+@GET_SEARCH_PARAMS_FUNCTION@\n+\n+input_json_path = sys.argv[1]\n+with open(input_json_path, "r") as param_handler:\n+    params = json.load(param_handler)\n+\n+#handle cheatah\n+infile1 = "$input_options.infile1"\n+infile2 = "$input_options.infile2"\n+infile_pipeline = "$search_schemes.infile_pipeline"\n+outfile_result = "$outfile_result"\n+outfile_estimator = "$outfile_estimator"\n+#if $search_schemes.selected_search_scheme == "RandomizedSearchCV":\n+np.random.seed($search_schemes.random_seed)\n+#end if\n+\n+params_builder = params[\'search_schemes\'][\'search_params_builder\']\n+\n+input_type = params["input_options"]["selected_input"]\n+if input_type=="tabular":\n+    header = \'infer\' if params["input_options"]["header1"] else None\n+    column_option = params["input_options"]["column_selector_options_1"]["selected_column_selector_option"]\n+    if column_option in ["by_index_number", "all_but_by_index_number", "by_header_name", "all_but_by_header_name"]:\n+        c = params["input_options"]["column_selector_options_1"]["col1"]\n+    else:\n+        c = None\n+    X = read_columns(\n+            infile1,\n+            c = c,\n+            c_option = column_option,\n+            sep=\'\\t\',\n+            header=header,\n+            parse_dates=True\n+    )\n+else:\n+    X = mmread(open("$input_options.infile1", \'r\'))\n+\n+header = \'infer\' if params["input_options"]["header2"] else None\n+column_option = params["input_options"]["column_selector_options_2"]["selected_column_selector_option2"]\n+if column_option in ["by_index_number", "all_but_by_index_number", "by_header_name", "all_but_by_header_name"]:\n+    c = params["input_options"]["column_selector_options_2"]["col2"]\n+else:\n+    c = None\n+y = read_columns(\n+        infile2,\n+        c = c,\n+        c_option = column_option,\n+        sep=\'\\t\',\n+        header=header,\n+        parse_dates=True\n+)\n+y=y.ravel()\n+\n+optimizers = params["search_schemes"]["selected_search_scheme"]\n+optimizers = getattr(model_selection, optimizers)\n+\n+options = params["search_schemes"]["options"]\n+if \'scoring\' in options and options[\'scoring\'] == \'\':\n+    options[\'scoring\'] = None\n+if \'pre_dispatch\' in options and options[\'pre_dispatch\'] == \'\':\n+    options[\'pre_dispatch\'] = None\n+\n+with open(infile_pipeline, \'rb\') as pipeline_handler:\n+    pipeline = pickle.load(pipeline_handler)\n+search_params = get_search_params(params_builder)\n+searcher = optimizers(pipeline, search_params, **options)\n+\n+searcher.fit(X, y)\n+\n+cv_result = pandas.DataFrame(searcher.cv_results_)\n+cv_result.to_csv(path_or_buf=outfile_result, sep=\'\\t\', header=True, index=False)\n+\n+#if $save:\n+with open(outfile_estimator, "wb") as output_handler:\n+    pickle.dump(searcher.best_estimator_, output_handler, pickle.HIGHEST_PROTOCOL)\n+#end if\n+\n+            ]]>\n+        </configfile>\n+    </configfiles>\n+    <inputs>\n+        <conditional name="search_schemes">\n+            <param name="selected_search_scheme" type="select" label="Select a mo'..b'  <conditional name="search_param_selector">\n+                <param name="search_p" value="linkage: [\'ward\', \'complete\', \'average\']"/>\n+                <param name="selected_param_type" value="prep_1_p"/>\n+            </conditional>\n+            <param name="infile1" value="regression_X.tabular" ftype="tabular"/>\n+            <param name="header1" value="true" />\n+            <param name="selected_column_selector_option" value="all_columns"/>\n+            <param name="infile2" value="regression_y.tabular" ftype="tabular"/>\n+            <param name="header2" value="true" />\n+            <param name="selected_column_selector_option2" value="all_columns"/>\n+            <output name="outfile_result">\n+                <assert_contents>\n+                    <has_text_matching expression=".+0.08045977011494253[^/w]+10[^/w]" />\n+                </assert_contents>\n+            </output>\n+        </test>\n+    </tests>\n+    <help>\n+        <![CDATA[\n+**What it does**\n+Searches optimized parameter values for an estimator or pipeline through either exhaustive grid cross validation search or Randomized cross validation search.\n+please refer to `Scikit-learn model_selection GridSearchCV`_, `Scikit-learn model_selection RandomizedSearchCV`_ and `Tuning hyper-parameters`_.\n+\n+**How to choose search patameters?**\n+\n+Please refer to `svm`_, `linear_model`_, `ensemble`_, `naive_bayes`_, `tree`_, `neighbors`_ and `xgboost`_ for estimator parameters.\n+Refer to `sklearn.preprocessing`_, `feature_selection`_, `decomposition`_, `kernel_approximation`_ and `cluster.FeatureAgglomeration`_ for parameter in the pre-processing steps.\n+\n+**Search parameter input** accepts parameter and setting in key:value pair. One pair per input box. Setting can be list, numpy array, or distribution.\n+The evaluation of settings supports operations in Math, list comprehension, numpy.arange(np_arange), most numpy.random(e.g., np_random_uniform) and some scipy.stats(e.g., scipy_stats_zipf) classes or functions, and others.\n+\n+**Examples:**\n+\n+- K: [3, 5, 7, 9]\n+\n+- n_estimators: list(range(50, 1001, 50))\n+\n+- gamma: np_arange(0.01, 1, 0.1)\n+\n+- alpha: np_random_choice(list(range(1, 51)) + [None], size=20)\n+\n+- max_depth: scipy_stats_randin(1, 11)\n+\n+- estimator: [ensemble_ExtraTreesClassifier(n_estimators=100, random_state=324089)]\n+\n+\n+.. _`Scikit-learn model_selection GridSearchCV`: http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html\n+.. _`Scikit-learn model_selection RandomizedSearchCV`: http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.RandomizedSearchCV.html\n+.. _`Tuning hyper-parameters`: http://scikit-learn.org/stable/modules/grid_search.html\n+\n+.. _`svm`: http://scikit-learn.org/stable/modules/classes.html#module-sklearn.svm\n+.. _`linear_model`: http://scikit-learn.org/stable/modules/classes.html#module-sklearn.linear_model\n+.. _`ensemble`: http://scikit-learn.org/stable/modules/classes.html#module-sklearn.ensemble\n+.. _`naive_bayes`: http://scikit-learn.org/stable/modules/classes.html#module-sklearn.naive_bayes\n+.. _`tree`: http://scikit-learn.org/stable/modules/classes.html#module-sklearn.tree\n+.. _`neighbors`: http://scikit-learn.org/stable/modules/classes.html#module-sklearn.neighbors\n+.. _`xgboost`: https://xgboost.readthedocs.io/en/latest/python/python_api.html\n+\n+.. _`sklearn.preprocessing`: http://scikit-learn.org/stable/modules/classes.html#module-sklearn.preprocessing\n+.. _`feature_selection`: http://scikit-learn.org/stable/modules/classes.html#module-sklearn.feature_selection\n+.. _`decomposition`: http://scikit-learn.org/stable/modules/classes.html#module-sklearn.decomposition\n+.. _`kernel_approximation`: http://scikit-learn.org/stable/modules/classes.html#module-sklearn.kernel_approximation\n+.. _`cluster.FeatureAgglomeration`: http://scikit-learn.org/stable/modules/generated/sklearn.cluster.FeatureAgglomeration.html\n+\n+        ]]>\n+    </help>\n+    <expand macro="sklearn_citation"/>\n+</tool>\n'
b
diff -r 000000000000 -r 91bf3f0d7455 test-data/RF01704.fasta
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/RF01704.fasta Sat Aug 04 12:31:24 2018 -0400
b
@@ -0,0 +1,4 @@
+>CP000097.1/1411351-1411410
+CAACGUUCACCUCACAUUUGUGAGGCGCAGACAACCCAGGCCAAGGAACGGGGACCUGGA
+>ACNY01000002.1/278641-278580
+GAUCGUUCACUUCGCAUCGCGCGAAGCGCAGUUCGCCUCAGGCCAUGGAACGGGGACCUGAG
b
diff -r 000000000000 -r 91bf3f0d7455 test-data/accuracy_score.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/accuracy_score.txt Sat Aug 04 12:31:24 2018 -0400
b
@@ -0,0 +1,2 @@
+accuracy_score : 
+0.8461538461538461
b
diff -r 000000000000 -r 91bf3f0d7455 test-data/auc.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/auc.txt Sat Aug 04 12:31:24 2018 -0400
b
@@ -0,0 +1,2 @@
+auc : 
+2.5
b
diff -r 000000000000 -r 91bf3f0d7455 test-data/average_precision_score.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/average_precision_score.txt Sat Aug 04 12:31:24 2018 -0400
b
@@ -0,0 +1,2 @@
+average_precision_score : 
+1.0
b
diff -r 000000000000 -r 91bf3f0d7455 test-data/blobs.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/blobs.txt Sat Aug 04 12:31:24 2018 -0400
b
@@ -0,0 +1,101 @@
+0 1 0
+0.3368184589673989 -3.402879612990731 0
+-9.48324265575857 -8.66266051536995 2
+-1.9333632849607592 5.709539081468901 1
+-10.031824059894126 -5.578343934583625 2
+0.5418607766170062 -4.3769362832693 0
+-8.129629290673781 -7.05554320549807 2
+-0.7308257856942714 7.323755516994815 1
+-1.8414253200701474 6.206304668308321 1
+0.4100718503166844 -3.997448810001185 0
+-8.735095893232405 -5.490905352087513 2
+1.8494196227705402 -3.918393456729695 0
+-9.152560688488569 -9.17805648051067 2
+-3.214299390778301 5.7592616395707115 1
+0.2845037854966439 -3.615765226438298 0
+-0.9290748492230643 5.79099955373578 1
+0.366925241944827 6.518619296229099 1
+1.5990991796541174 -3.0710561729787464 0
+-9.71270568435724 -7.917076514990086 2
+-10.080404430632045 -6.551353241086546 2
+1.1059434577429306 -4.419063749495465 0
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b
diff -r 000000000000 -r 91bf3f0d7455 test-data/brier_score_loss.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/brier_score_loss.txt Sat Aug 04 12:31:24 2018 -0400
b
@@ -0,0 +1,2 @@
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b
diff -r 000000000000 -r 91bf3f0d7455 test-data/circles.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/circles.txt Sat Aug 04 12:31:24 2018 -0400
b
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diff -r 000000000000 -r 91bf3f0d7455 test-data/class.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/class.txt Sat Aug 04 12:31:24 2018 -0400
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b
diff -r 000000000000 -r 91bf3f0d7455 test-data/classification_report.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/classification_report.txt Sat Aug 04 12:31:24 2018 -0400
b
@@ -0,0 +1,9 @@
+classification_report : 
+             precision    recall  f1-score   support
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b
diff -r 000000000000 -r 91bf3f0d7455 test-data/cluster_result01.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/cluster_result01.txt Sat Aug 04 12:31:24 2018 -0400
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b
diff -r 000000000000 -r 91bf3f0d7455 test-data/cluster_result02.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/cluster_result02.txt Sat Aug 04 12:31:24 2018 -0400
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b
diff -r 000000000000 -r 91bf3f0d7455 test-data/cluster_result03.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/cluster_result03.txt Sat Aug 04 12:31:24 2018 -0400
b
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b
diff -r 000000000000 -r 91bf3f0d7455 test-data/cluster_result04.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/cluster_result04.txt Sat Aug 04 12:31:24 2018 -0400
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b
diff -r 000000000000 -r 91bf3f0d7455 test-data/cluster_result05.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/cluster_result05.txt Sat Aug 04 12:31:24 2018 -0400
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b
diff -r 000000000000 -r 91bf3f0d7455 test-data/cluster_result06.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/cluster_result06.txt Sat Aug 04 12:31:24 2018 -0400
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diff -r 000000000000 -r 91bf3f0d7455 test-data/cluster_result07.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/cluster_result07.txt Sat Aug 04 12:31:24 2018 -0400
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diff -r 000000000000 -r 91bf3f0d7455 test-data/cluster_result08.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/cluster_result08.txt Sat Aug 04 12:31:24 2018 -0400
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diff -r 000000000000 -r 91bf3f0d7455 test-data/cluster_result09.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/cluster_result09.txt Sat Aug 04 12:31:24 2018 -0400
b
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diff -r 000000000000 -r 91bf3f0d7455 test-data/cluster_result10.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/cluster_result10.txt Sat Aug 04 12:31:24 2018 -0400
b
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diff -r 000000000000 -r 91bf3f0d7455 test-data/cluster_result11.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/cluster_result11.txt Sat Aug 04 12:31:24 2018 -0400
b
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diff -r 000000000000 -r 91bf3f0d7455 test-data/cluster_result12
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/cluster_result12 Sat Aug 04 12:31:24 2018 -0400
b
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b
diff -r 000000000000 -r 91bf3f0d7455 test-data/cluster_result12.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/cluster_result12.txt Sat Aug 04 12:31:24 2018 -0400
b
@@ -0,0 +1,48 @@
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diff -r 000000000000 -r 91bf3f0d7455 test-data/cluster_result13.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/cluster_result13.txt Sat Aug 04 12:31:24 2018 -0400
b
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diff -r 000000000000 -r 91bf3f0d7455 test-data/cluster_result14.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/cluster_result14.txt Sat Aug 04 12:31:24 2018 -0400
b
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diff -r 000000000000 -r 91bf3f0d7455 test-data/cluster_result15.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/cluster_result15.txt Sat Aug 04 12:31:24 2018 -0400
b
@@ -0,0 +1,48 @@
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diff -r 000000000000 -r 91bf3f0d7455 test-data/cluster_result16.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/cluster_result16.txt Sat Aug 04 12:31:24 2018 -0400
b
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diff -r 000000000000 -r 91bf3f0d7455 test-data/cluster_result17.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/cluster_result17.txt Sat Aug 04 12:31:24 2018 -0400
b
@@ -0,0 +1,4 @@
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b
diff -r 000000000000 -r 91bf3f0d7455 test-data/cluster_result18.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/cluster_result18.txt Sat Aug 04 12:31:24 2018 -0400
b
@@ -0,0 +1,4 @@
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b
diff -r 000000000000 -r 91bf3f0d7455 test-data/cluster_result19.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/cluster_result19.txt Sat Aug 04 12:31:24 2018 -0400
b
@@ -0,0 +1,4 @@
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b
diff -r 000000000000 -r 91bf3f0d7455 test-data/cluster_result20.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/cluster_result20.txt Sat Aug 04 12:31:24 2018 -0400
b
@@ -0,0 +1,4 @@
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b
diff -r 000000000000 -r 91bf3f0d7455 test-data/cluster_result21.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/cluster_result21.txt Sat Aug 04 12:31:24 2018 -0400
b
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diff -r 000000000000 -r 91bf3f0d7455 test-data/confusion_matrix.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/confusion_matrix.txt Sat Aug 04 12:31:24 2018 -0400
[
@@ -0,0 +1,4 @@
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+[[14  0  0]
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b
diff -r 000000000000 -r 91bf3f0d7455 test-data/converter_result01.json
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/converter_result01.json Sat Aug 04 12:31:24 2018 -0400
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b
diff -r 000000000000 -r 91bf3f0d7455 test-data/converter_result02.json
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/converter_result02.json Sat Aug 04 12:31:24 2018 -0400
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b
diff -r 000000000000 -r 91bf3f0d7455 test-data/csc_sparse1.mtx
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/csc_sparse1.mtx Sat Aug 04 12:31:24 2018 -0400
b
@@ -0,0 +1,9 @@
+%%MatrixMarket matrix coordinate integer general
+%
+3 3 6
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+3 2 3
+1 3 4
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+3 3 6
b
diff -r 000000000000 -r 91bf3f0d7455 test-data/csc_sparse2.mtx
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/csc_sparse2.mtx Sat Aug 04 12:31:24 2018 -0400
b
@@ -0,0 +1,9 @@
+%%MatrixMarket matrix coordinate real general
+%
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b
diff -r 000000000000 -r 91bf3f0d7455 test-data/csc_stack_result01.mtx
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/csc_stack_result01.mtx Sat Aug 04 12:31:24 2018 -0400
b
@@ -0,0 +1,15 @@
+%%MatrixMarket matrix coordinate real general
+%
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b
diff -r 000000000000 -r 91bf3f0d7455 test-data/csr_sparse1.mtx
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/csr_sparse1.mtx Sat Aug 04 12:31:24 2018 -0400
b
@@ -0,0 +1,9 @@
+%%MatrixMarket matrix coordinate integer general
+%
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b
diff -r 000000000000 -r 91bf3f0d7455 test-data/csr_sparse2.mtx
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/csr_sparse2.mtx Sat Aug 04 12:31:24 2018 -0400
b
@@ -0,0 +1,9 @@
+%%MatrixMarket matrix coordinate real general
+%
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b
diff -r 000000000000 -r 91bf3f0d7455 test-data/csr_stack_result01.mtx
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/csr_stack_result01.mtx Sat Aug 04 12:31:24 2018 -0400
b
@@ -0,0 +1,15 @@
+%%MatrixMarket matrix coordinate real general
+%
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b
diff -r 000000000000 -r 91bf3f0d7455 test-data/empty_file.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/empty_file.txt Sat Aug 04 12:31:24 2018 -0400
b
@@ -0,0 +1,48 @@
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b
diff -r 000000000000 -r 91bf3f0d7455 test-data/f1_score.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/f1_score.txt Sat Aug 04 12:31:24 2018 -0400
b
@@ -0,0 +1,2 @@
+f1_score : 
+0.8461538461538461
b
diff -r 000000000000 -r 91bf3f0d7455 test-data/fbeta_score.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/fbeta_score.txt Sat Aug 04 12:31:24 2018 -0400
b
@@ -0,0 +1,2 @@
+fbeta_score : 
+0.8461538461538461
b
diff -r 000000000000 -r 91bf3f0d7455 test-data/feature_selection_result01
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/feature_selection_result01 Sat Aug 04 12:31:24 2018 -0400
b
@@ -0,0 +1,11 @@
+0 1
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b
diff -r 000000000000 -r 91bf3f0d7455 test-data/feature_selection_result02
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/feature_selection_result02 Sat Aug 04 12:31:24 2018 -0400
b
@@ -0,0 +1,262 @@
+temp_2 temp_1 forecast_noaa friend
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diff -r 000000000000 -r 91bf3f0d7455 test-data/feature_selection_result03
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
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diff -r 000000000000 -r 91bf3f0d7455 test-data/feature_selection_result04
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/feature_selection_result04 Sat Aug 04 12:31:24 2018 -0400
b
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b
diff -r 000000000000 -r 91bf3f0d7455 test-data/feature_selection_result05
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/feature_selection_result05 Sat Aug 04 12:31:24 2018 -0400
b
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b
diff -r 000000000000 -r 91bf3f0d7455 test-data/feature_selection_result06
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/feature_selection_result06 Sat Aug 04 12:31:24 2018 -0400
b
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b
diff -r 000000000000 -r 91bf3f0d7455 test-data/feature_selection_result07
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/feature_selection_result07 Sat Aug 04 12:31:24 2018 -0400
b
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diff -r 000000000000 -r 91bf3f0d7455 test-data/feature_selection_result08
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/feature_selection_result08 Sat Aug 04 12:31:24 2018 -0400
b
@@ -0,0 +1,11 @@
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b
diff -r 000000000000 -r 91bf3f0d7455 test-data/feature_selection_result09
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/feature_selection_result09 Sat Aug 04 12:31:24 2018 -0400
b
@@ -0,0 +1,11 @@
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b
diff -r 000000000000 -r 91bf3f0d7455 test-data/feature_selection_result10
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/feature_selection_result10 Sat Aug 04 12:31:24 2018 -0400
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b
diff -r 000000000000 -r 91bf3f0d7455 test-data/feature_selection_result11
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/feature_selection_result11 Sat Aug 04 12:31:24 2018 -0400
b
@@ -0,0 +1,51 @@
+Race AIDS Total
+4.0 2555.0 14443382.0
+4.0 55300.0 14704293.0
+4.0 82334.0 16641977.0
+4.0 38006.0 13888285.0
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+3.0 493.0 1601948.0
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+3.0 5153.0 1845800.0
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+3.0 910.0 2422980.0
+1.0 6.0 726561.0
+1.0 83.0 739686.0
+1.0 106.0 837159.0
+1.0 69.0 698637.0
+1.0 55.0 1098938.0
+0.0 3.0 175626.0
+0.0 78.0 178798.0
+0.0 77.0 202360.0
+0.0 31.0 168876.0
+0.0 14.0 265637.0
b
diff -r 000000000000 -r 91bf3f0d7455 test-data/feature_selection_result12
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/feature_selection_result12 Sat Aug 04 12:31:24 2018 -0400
b
@@ -0,0 +1,11 @@
+0 1
+143.762620712 -1.1796457192799998
+-88.5787166225 -2.5710918402200003
+-82.8452345578 -0.168636324107
+72.4951388149 0.991068834926
+11.805182128 -0.7096855607860001
+-63.9354970901 0.9841122108220001
+126.32584079600001 0.35353444883900004
+23.0341392692 1.03188231893
+67.6714937696 -0.8214378651719999
+47.39275848810001 -0.0942409319417
b
diff -r 000000000000 -r 91bf3f0d7455 test-data/friedman1.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/friedman1.txt Sat Aug 04 12:31:24 2018 -0400
b
b'@@ -0,0 +1,101 @@\n+0\t1\t2\t3\t4\t5\t6\t7\t8\t9\t0\n+0.5434049417909654\t0.27836938509379616\t0.4245175907491331\t0.8447761323199037\t0.004718856190972565\t0.12156912078311422\t0.6707490847267786\t0.8258527551050476\t0.13670658968495297\t0.57509332942725\t13.160650397398078\n+0.891321954312264\t0.20920212211718958\t0.18532821955007506\t0.10837689046425514\t0.21969749262499216\t0.9786237847073697\t0.8116831490893233\t0.1719410127325942\t0.8162247487258399\t0.2740737470416992\t9.691298137658498\n+0.4317041836631217\t0.9400298196223746\t0.8176493787767274\t0.3361119501208987\t0.17541045374233666\t0.37283204628992317\t0.005688507352573424\t0.25242635344484043\t0.7956625084732873\t0.01525497124633901\t15.821619961828777\n+0.5988433769284929\t0.6038045390428536\t0.10514768541205632\t0.38194344494311006\t0.03647605659256892\t0.8904115634420757\t0.9809208570123115\t0.05994198881803725\t0.8905459447285041\t0.5769014994000329\t16.18933274618261\n+0.7424796890979773\t0.6301839364753761\t0.5818421923987779\t0.020439132026923157\t0.2100265776728606\t0.5446848781786475\t0.7691151711056516\t0.2506952291383959\t0.2858956904068647\t0.8523950878413064\t11.33767760089345\n+0.9750064936065875\t0.8848532934911055\t0.35950784393690227\t0.5988589458757472\t0.3547956116572998\t0.34019021537064575\t0.17808098950580487\t0.23769420862405044\t0.04486228246077528\t0.5054314296357892\t12.337142824178603\n+0.376252454297363\t0.5928054009758866\t0.6299418755874974\t0.14260031444628352\t0.933841299466419\t0.9463798808091013\t0.6022966577308656\t0.38776628032663074\t0.3631880041093498\t0.20434527686864423\t12.880550712301464\n+0.27676506139633517\t0.24653588120354963\t0.17360800174020508\t0.9666096944873236\t0.9570126003527981\t0.5979736843289207\t0.7313007530599226\t0.3403852228374361\t0.09205560337723862\t0.4634980189371477\t18.709003936604173\n+0.508698893238194\t0.08846017300289077\t0.5280352233180474\t0.9921580365105283\t0.3950359317582296\t0.3355964417185683\t0.8054505373292797\t0.7543489945823536\t0.3130664415885097\t0.6340366829622751\t13.321479131556272\n+0.5404045753007164\t0.2967937508800147\t0.11078790118244575\t0.3126402978757431\t0.4569791300492658\t0.6589400702261969\t0.2542575178177181\t0.6411012587007017\t0.20012360721840317\t0.6576248055289837\t13.269253863108887\n+0.7782892154498485\t0.7795983986107496\t0.6103281532093938\t0.30900034852440217\t0.697734907512956\t0.8596182957290651\t0.6253237577568076\t0.9824078296095496\t0.9765001270158553\t0.16669413119885812\t16.264995170787344\n+0.02317813647840361\t0.16074454850708164\t0.9234968252590874\t0.953549849879534\t0.21097841871844636\t0.3605252508146082\t0.5493752616276721\t0.2718308491769721\t0.46060162107485014\t0.6961615648233853\t14.294427313499119\n+0.500355896674865\t0.7160709905643361\t0.525955936229779\t0.0013990231190438296\t0.3947002866898355\t0.49216696990114994\t0.40288033137914214\t0.3542983001063206\t0.5006143194429532\t0.4451766288311383\t11.026237192296225\n+0.09043278819643585\t0.27356292002744065\t0.9434770977427269\t0.026544641333941965\t0.039998689640650786\t0.28314035971981955\t0.5823441702167689\t0.9908928029248271\t0.9926422374029681\t0.9931173724810448\t5.175296804362765\n+0.11004833096656297\t0.6644814459639401\t0.5239868344883128\t0.17314990980873102\t0.9429602449150257\t0.2418600859762523\t0.998932268843212\t0.5826938151498987\t0.18327900063057578\t0.38684542191779026\t8.73494610704017\n+0.1896735289121496\t0.41077067302531\t0.5946800689017054\t0.7165860931283399\t0.4868914823691235\t0.3095898177667046\t0.5774413728278474\t0.44170781956874294\t0.35967810260053623\t0.3213319320088136\t12.202924702612343\n+0.20820724019602266\t0.45125862406183437\t0.4918429102640539\t0.8990763147937112\t0.7293604610294412\t0.7700897729196955\t0.3754392475619882\t0.34373953523538436\t0.6550352059993224\t0.7110379932104975\t15.547914734580145\n+0.11353757521867625\t0.13302868937357504\t0.45603905760612395\t0.15973623015851013\t0.9616419037746458\t0.8376157448618098\t0.5201606870379233\t0.2182722577281544\t0.13491872253239878\t0.9790703454838688\t6.918543527074358\n+0.7070434956891432\t0.8599755569456631\t0.38717262782863904\t0.2508340198317248\t0.29943801894470223\t0.8568955284050157\t0.47298399056'..b'7639289\t0.8212053023334988\t0.48801691478932074\t0.7570620648656129\t0.37107807260930714\t0.2695048247626399\t0.7345946354267014\t0.8465645262987437\t19.45300037464767\n+0.7731597126964512\t0.09726311997082848\t0.3128848054042207\t0.054297371248047455\t0.9964178644970716\t0.177698734352288\t0.37123100482185356\t0.3589325920964376\t0.23918094189867656\t0.19412444639857296\t8.565865450206013\n+0.7221568697894736\t0.9963498623999889\t0.6578810615587287\t0.18964066816521796\t0.7960500133787225\t0.6331488340440541\t0.05997465943644009\t0.45123696414113945\t0.3981555798526656\t0.4574877112189486\t14.089903184543674\n+0.17329540858703296\t0.5551602246692062\t0.675575702816968\t0.8264278406303862\t0.753975346409477\t0.03806626488278475\t0.795113651901603\t0.6539318070808459\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b
diff -r 000000000000 -r 91bf3f0d7455 test-data/friedman2.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/friedman2.txt Sat Aug 04 12:31:24 2018 -0400
b
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b
diff -r 000000000000 -r 91bf3f0d7455 test-data/friedman3.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/friedman3.txt Sat Aug 04 12:31:24 2018 -0400
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diff -r 000000000000 -r 91bf3f0d7455 test-data/gaus.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
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diff -r 000000000000 -r 91bf3f0d7455 test-data/gbc_model01
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diff -r 000000000000 -r 91bf3f0d7455 test-data/gbc_result01
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
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diff -r 000000000000 -r 91bf3f0d7455 test-data/gbr_model01
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Binary file test-data/gbr_model01 has changed
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diff -r 000000000000 -r 91bf3f0d7455 test-data/gbr_prediction_result01.tabular
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/gbr_prediction_result01.tabular Sat Aug 04 12:31:24 2018 -0400
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b
diff -r 000000000000 -r 91bf3f0d7455 test-data/hinge_loss.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/hinge_loss.txt Sat Aug 04 12:31:24 2018 -0400
b
@@ -0,0 +1,2 @@
+hinge_loss : 
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b
diff -r 000000000000 -r 91bf3f0d7455 test-data/jaccard_similarity_score.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/jaccard_similarity_score.txt Sat Aug 04 12:31:24 2018 -0400
b
@@ -0,0 +1,2 @@
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b
diff -r 000000000000 -r 91bf3f0d7455 test-data/lda_model01
b
Binary file test-data/lda_model01 has changed
b
diff -r 000000000000 -r 91bf3f0d7455 test-data/lda_model02
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Binary file test-data/lda_model02 has changed
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diff -r 000000000000 -r 91bf3f0d7455 test-data/lda_prediction_result01.tabular
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/lda_prediction_result01.tabular Sat Aug 04 12:31:24 2018 -0400
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b
diff -r 000000000000 -r 91bf3f0d7455 test-data/lda_prediction_result02.tabular
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/lda_prediction_result02.tabular Sat Aug 04 12:31:24 2018 -0400
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diff -r 000000000000 -r 91bf3f0d7455 test-data/log_loss.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/log_loss.txt Sat Aug 04 12:31:24 2018 -0400
b
@@ -0,0 +1,2 @@
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b
diff -r 000000000000 -r 91bf3f0d7455 test-data/matthews_corrcoef.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/matthews_corrcoef.txt Sat Aug 04 12:31:24 2018 -0400
b
@@ -0,0 +1,2 @@
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b
diff -r 000000000000 -r 91bf3f0d7455 test-data/moons.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/moons.txt Sat Aug 04 12:31:24 2018 -0400
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b
diff -r 000000000000 -r 91bf3f0d7455 test-data/mv_result01.tabular
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/mv_result01.tabular Sat Aug 04 12:31:24 2018 -0400
b
@@ -0,0 +1,3 @@
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b
diff -r 000000000000 -r 91bf3f0d7455 test-data/mv_result02.tabular
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/mv_result02.tabular Sat Aug 04 12:31:24 2018 -0400
b
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diff -r 000000000000 -r 91bf3f0d7455 test-data/mv_result03.tabular
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/mv_result03.tabular Sat Aug 04 12:31:24 2018 -0400
b
@@ -0,0 +1,3 @@
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diff -r 000000000000 -r 91bf3f0d7455 test-data/mv_result04.tabular
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/mv_result04.tabular Sat Aug 04 12:31:24 2018 -0400
b
@@ -0,0 +1,5 @@
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diff -r 000000000000 -r 91bf3f0d7455 test-data/mv_result05.tabular
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/mv_result05.tabular Sat Aug 04 12:31:24 2018 -0400
b
@@ -0,0 +1,1 @@
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b
diff -r 000000000000 -r 91bf3f0d7455 test-data/mv_result06.tabular
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/mv_result06.tabular Sat Aug 04 12:31:24 2018 -0400
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diff -r 000000000000 -r 91bf3f0d7455 test-data/nn_model01.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/nn_model01.txt Sat Aug 04 12:31:24 2018 -0400
[
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b
diff -r 000000000000 -r 91bf3f0d7455 test-data/nn_model02.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/nn_model02.txt Sat Aug 04 12:31:24 2018 -0400
[
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b
diff -r 000000000000 -r 91bf3f0d7455 test-data/nn_model03.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/nn_model03.txt Sat Aug 04 12:31:24 2018 -0400
b
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\ No newline at end of file
b
diff -r 000000000000 -r 91bf3f0d7455 test-data/nn_prediction_result01.tabular
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/nn_prediction_result01.tabular Sat Aug 04 12:31:24 2018 -0400
b
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diff -r 000000000000 -r 91bf3f0d7455 test-data/nn_prediction_result02.tabular
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/nn_prediction_result02.tabular Sat Aug 04 12:31:24 2018 -0400
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b
diff -r 000000000000 -r 91bf3f0d7455 test-data/nn_prediction_result03.tabular
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/nn_prediction_result03.tabular Sat Aug 04 12:31:24 2018 -0400
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diff -r 000000000000 -r 91bf3f0d7455 test-data/numeric_values.tabular
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diff -r 000000000000 -r 91bf3f0d7455 test-data/precision_recall_curve.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
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diff -r 000000000000 -r 91bf3f0d7455 test-data/precision_recall_fscore_support.txt
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diff -r 000000000000 -r 91bf3f0d7455 test-data/prp_result01
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diff -r 000000000000 -r 91bf3f0d7455 test-data/prp_result08
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/prp_result08 Sat Aug 04 12:31:24 2018 -0400
b
@@ -0,0 +1,9 @@
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b
diff -r 000000000000 -r 91bf3f0d7455 test-data/prp_result09
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/prp_result09 Sat Aug 04 12:31:24 2018 -0400
b
@@ -0,0 +1,9 @@
+%%MatrixMarket matrix coordinate real general
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b
diff -r 000000000000 -r 91bf3f0d7455 test-data/pw_metric01.tabular
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/pw_metric01.tabular Sat Aug 04 12:31:24 2018 -0400
b
@@ -0,0 +1,4 @@
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b
diff -r 000000000000 -r 91bf3f0d7455 test-data/pw_metric02.tabular
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/pw_metric02.tabular Sat Aug 04 12:31:24 2018 -0400
b
@@ -0,0 +1,4 @@
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b
diff -r 000000000000 -r 91bf3f0d7455 test-data/pw_metric03.tabular
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/pw_metric03.tabular Sat Aug 04 12:31:24 2018 -0400
b
@@ -0,0 +1,4 @@
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b
diff -r 000000000000 -r 91bf3f0d7455 test-data/qda_model01
b
Binary file test-data/qda_model01 has changed
b
diff -r 000000000000 -r 91bf3f0d7455 test-data/qda_prediction_result01.tabular
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/qda_prediction_result01.tabular Sat Aug 04 12:31:24 2018 -0400
b
@@ -0,0 +1,5 @@
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b
diff -r 000000000000 -r 91bf3f0d7455 test-data/recall_score.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/recall_score.txt Sat Aug 04 12:31:24 2018 -0400
b
@@ -0,0 +1,2 @@
+recall_score : 
+0.8461538461538461
b
diff -r 000000000000 -r 91bf3f0d7455 test-data/regression.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/regression.txt Sat Aug 04 12:31:24 2018 -0400
b
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b
diff -r 000000000000 -r 91bf3f0d7455 test-data/regression_X.tabular
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/regression_X.tabular Sat Aug 04 12:31:24 2018 -0400
b
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t0\t0\t0\t0\n+2016\t4\t12\t59\t58\t57.7\t54\t59\t57\t61\t0\t0\t0\t0\t0\t1\t0\n+2016\t3\t31\t64\t68\t55.9\t55\t59\t56\t56\t0\t0\t0\t0\t1\t0\t0\n+2016\t12\t14\t43\t40\t45.4\t45\t48\t45\t49\t0\t0\t0\t0\t0\t0\t1\n+2016\t8\t5\t75\t80\t77.3\t75\t81\t78\t71\t1\t0\t0\t0\t0\t0\t0\n+2016\t5\t4\t87\t74\t62.3\t59\t65\t64\t61\t0\t0\t0\t0\t0\t0\t1\n+2016\t12\t31\t48\t57\t45.5\t42\t48\t47\t57\t0\t0\t1\t0\t0\t0\t0\n+2016\t1\t21\t48\t52\t47.8\t43\t51\t46\t57\t0\t0\t0\t0\t1\t0\t0\n+2016\t7\t10\t74\t71\t75.1\t71\t77\t76\t95\t0\t0\t0\t1\t0\t0\t0\n+2016\t3\t15\t54\t49\t53.6\t49\t58\t52\t70\t0\t0\t0\t0\t0\t1\t0\n+2016\t4\t19\t77\t89\t59.0\t59\t63\t59\t61\t0\t0\t0\t0\t0\t1\t0\n+2016\t10\t14\t66\t60\t60.2\t56\t64\t60\t78\t1\t0\t0\t0\t0\t0\t0\n+2016\t4\t15\t59\t59\t58.3\t58\t61\t60\t40\t1\t0\t0\t0\t0\t0\t0\n'
b
diff -r 000000000000 -r 91bf3f0d7455 test-data/regression_metrics_result01
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/regression_metrics_result01 Sat Aug 04 12:31:24 2018 -0400
b
@@ -0,0 +1,2 @@
+explained_variance_score : 
+0.8260
b
diff -r 000000000000 -r 91bf3f0d7455 test-data/regression_metrics_result02
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/regression_metrics_result02 Sat Aug 04 12:31:24 2018 -0400
b
@@ -0,0 +1,2 @@
+mean_absolute_error : 
+3.8706
b
diff -r 000000000000 -r 91bf3f0d7455 test-data/regression_metrics_result03
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/regression_metrics_result03 Sat Aug 04 12:31:24 2018 -0400
b
@@ -0,0 +1,2 @@
+mean_squared_error : 
+26.0153
b
diff -r 000000000000 -r 91bf3f0d7455 test-data/regression_metrics_result04
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/regression_metrics_result04 Sat Aug 04 12:31:24 2018 -0400
b
@@ -0,0 +1,2 @@
+mean_squared_log_error : 
+0.0061
b
diff -r 000000000000 -r 91bf3f0d7455 test-data/regression_metrics_result05
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/regression_metrics_result05 Sat Aug 04 12:31:24 2018 -0400
b
@@ -0,0 +1,2 @@
+median_absolute_error : 
+3.0090
b
diff -r 000000000000 -r 91bf3f0d7455 test-data/regression_metrics_result06
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/regression_metrics_result06 Sat Aug 04 12:31:24 2018 -0400
b
@@ -0,0 +1,2 @@
+r2_score : 
+0.8129
b
diff -r 000000000000 -r 91bf3f0d7455 test-data/regression_test.tabular
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/regression_test.tabular Sat Aug 04 12:31:24 2018 -0400
b
@@ -0,0 +1,5 @@
+86.9702122735 1.00532111569 -1.01739601979 -0.613139481654 0.641846874331
+91.2021798817 -0.621522971207 1.11914889596 0.390012184498 1.28956938152
+-47.4101632272 -0.638416457964 -0.732777468453 -0.864026104978 -1.06109770116
+61.7128046302 -1.09994800577 -0.739679672932 0.585657963012 1.48906827536
+-206.998295124 0.130238853011 0.70574123041 1.33206565264 -1.33220923738
b
diff -r 000000000000 -r 91bf3f0d7455 test-data/regression_test_X.tabular
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/regression_test_X.tabular Sat Aug 04 12:31:24 2018 -0400
b
@@ -0,0 +1,88 @@
+year month day temp_2 temp_1 average forecast_noaa forecast_acc forecast_under friend week_Fri week_Mon week_Sat week_Sun week_Thurs week_Tues week_Wed
+2016 9 29 69 68 66.1 63 71 68 57 0 0 0 0 1 0 0
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+2016 12 22 51 49 45.1 42 47 46 38 0 0 0 0 1 0 0
b
diff -r 000000000000 -r 91bf3f0d7455 test-data/regression_test_y.tabular
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/regression_test_y.tabular Sat Aug 04 12:31:24 2018 -0400
b
@@ -0,0 +1,88 @@
+actual prediction
+66 69.857
+61 61.319
+52 51.891
+66 61.321
+70 66.463
+82 70.162
+85 78.848
+84 75.786
+65 62.121
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+++ b/test-data/sparse.mtx Sat Aug 04 12:31:24 2018 -0400
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b'@@ -0,0 +1,8741 @@\n+%%MatrixMarket matrix coordinate real general\n+%\n+4 1048577 8738\n+1 271 0.02083333333333341\n+1 1038 0.02461995616119806\n+1 1665 0.01253924656438802\n+1 2794 0.0250470072492813\n+1 2897 0.02083333333333341\n+1 3377 0.02083333333333341\n+1 4053 0.05769913639656241\n+1 4959 0.007693218186208322\n+1 5733 0.01641330410746537\n+1 5985 0.01294450932696249\n+1 6146 0.02461995616119806\n+1 6551 0.02083333333333341\n+1 6812 0.01252350362464065\n+1 7663 0.01252350362464065\n+1 8132 0.01941676399044373\n+1 8260 0.01294450932696249\n+1 8398 0.02083333333333341\n+1 8495 0.01253924656438802\n+1 8846 0.02083333333333341\n+1 8955 0.01641330410746537\n+1 9442 0.01941676399044373\n+1 9811 0.02461995616119806\n+1 10010 0.01252350362464065\n+1 10205 0.0194183909345155\n+1 10495 0.03816237987288836\n+1 12091 0.0980885318741561\n+1 12255 0.01641330410746537\n+1 12330 0.01294450932696249\n+1 12841 0.01941676399044373\n+1 13130 0.00970919546725775\n+1 13234 0.00763247597457767\n+1 13369 0.01252350362464065\n+1 13424 0.02461995616119806\n+1 13929 0.01252350362464065\n+1 14370 0.01252350362464065\n+1 14667 0.01641330410746537\n+1 15146 0.01253924656438802\n+1 15784 0.009940534656094338\n+1 15880 0.02083333333333341\n+1 17369 0.01252350362464065\n+1 17674 0.03236127331740622\n+1 18464 0.009940534656094338\n+1 19202 0.00970919546725775\n+1 19526 0.01252350362464065\n+1 19723 0.01253924656438802\n+1 19745 0.02083333333333341\n+1 20407 0.01641330410746537\n+1 20582 0.01252350362464065\n+1 20843 0.00970919546725775\n+1 20975 0.0692389636758749\n+1 21671 0.0152711805445382\n+1 21829 0.0250470072492813\n+1 22178 0.01538643637241664\n+1 22277 0.02083333333333341\n+1 22856 0.01641330410746537\n+1 23053 0.01641330410746537\n+1 23225 0.01294450932696249\n+1 23728 0.02083333333333341\n+1 24382 0.01294450932696249\n+1 24672 0.00970919546725775\n+1 25245 0.01252350362464065\n+1 26569 0.03054236108907641\n+1 27748 0.01252350362464065\n+1 27941 0.01252350362464065\n+1 28962 0.01252350362464065\n+1 29320 0.01252350362464065\n+1 29735 0.07635590272269102\n+1 29839 0.00970919546725775\n+1 30063 0.02083333333333341\n+1 30646 0.0250470072492813\n+1 31588 0.03130875906160163\n+1 32319 0.01294450932696249\n+1 32433 0.01294450932696249\n+1 32797 0.009940534656094338\n+1 32800 0.00970919546725775\n+1 32837 0.02083333333333341\n+1 33008 0.00970919546725775\n+1 33979 0.01880886984658204\n+1 35441 0.0194183909345155\n+1 36189 0.01641330410746537\n+1 37457 0.0152711805445382\n+1 38049 0.01294450932696249\n+1 38464 0.00970919546725775\n+1 39762 0.0194183909345155\n+1 40007 0.01514765184153846\n+1 40018 0.02461995616119806\n+1 40091 0.01294450932696249\n+1 40157 0.01880886984658204\n+1 40920 0.007693218186208322\n+1 41305 0.02083333333333341\n+1 41617 0.01294450932696249\n+1 41628 0.0250470072492813\n+1 41645 0.0152711805445382\n+1 41800 0.03713053286162541\n+1 41970 0.01294450932696249\n+1 42308 0.02083333333333341\n+1 43264 0.02083333333333341\n+1 43550 0.01252350362464065\n+1 43781 0.01526495194915534\n+1 43902 0.0250470072492813\n+1 44084 0.00970919546725775\n+1 44116 0.0250470072492813\n+1 44133 0.01294450932696249\n+1 44135 0.01641330410746537\n+1 44195 0.01294450932696249\n+1 44513 0.02083333333333341\n+1 44990 0.009940534656094338\n+1 45201 0.02083333333333341\n+1 45447 0.01880886984658204\n+1 45548 0.0152711805445382\n+1 46543 0.01252350362464065\n+1 46563 0.0152711805445382\n+1 46627 0.01009843456102564\n+1 46930 0.009940534656094338\n+1 47084 0.01253924656438802\n+1 48208 0.01252350362464065\n+1 48783 0.0152711805445382\n+1 48993 0.01641330410746537\n+1 50742 0.02500295910517705\n+1 52051 0.01880886984658204\n+1 52833 0.002524608640256409\n+1 53918 0.01294450932696249\n+1 54190 0.01252350362464065\n+1 54267 0.00970919546725775\n+1 54837 0.009940534656094338\n+1 55562 0.02588901865392498\n+1 55759 0.02083333333333341\n+1 55865 0.009940534656094338\n+1 56669 0.01294450932696249\n+1 57379 0.00970919546725775\n+1 57633 0.0194183909345155\n+1 58567 0.01641330410746537\n+1 58964 0.007693218186208322\n+1 59338 0.01641330410746537\n+1 60239 0.02083333333333341\n+1 60904 0.0152711805445382\n+'..b'4 983313 0.01343038273375637\n+4 983688 0.02083333333333338\n+4 983770 0.01362848167001797\n+4 984175 0.01662975263094352\n+4 984900 0.02686076546751275\n+4 985526 0.01343038273375637\n+4 985593 0.02531848417709173\n+4 985753 0.01343038273375637\n+4 985859 0.01641330410746536\n+4 986055 0.02083333333333338\n+4 986185 0.01253136767792717\n+4 987191 0.01056442818410648\n+4 987694 0.0221186977601905\n+4 989433 0.04423739552038099\n+4 989840 0.01253136767792717\n+4 990517 0.01641330410746536\n+4 990522 0.01362848167001797\n+4 991282 0.01253136767792717\n+4 991559 0.01265924208854587\n+4 991935 0.01641330410746536\n+4 992416 0.01327797629320365\n+4 993308 0.02506273535585434\n+4 993319 0.02054987341316971\n+4 994759 0.01641330410746536\n+4 995303 0.01679782851708494\n+4 996150 0.01343038273375637\n+4 996559 0.01343038273375637\n+4 997115 0.0208333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b
diff -r 000000000000 -r 91bf3f0d7455 test-data/sparse_u.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/sparse_u.txt Sat Aug 04 12:31:24 2018 -0400
b
b'@@ -0,0 +1,101 @@\n+0\t1\t2\t3\t4\t5\t6\t7\t8\t9\t0\n+-1.7497654730546974\t0.34268040332750216\t1.153035802563644\t-0.25243603652138985\t0.9813207869512316\t0.5142188413943821\t0.22117966922140045\t-1.0700433305682933\t-0.18949583082317534\t0.25500144427338167\t-3.2026895707176237\n+-0.4580269855026243\t0.43516348812289213\t-0.5835950503226648\t0.816847071685779\t0.672720805709661\t-0.10441114339062771\t-0.5312803768519098\t1.0297326851333461\t-0.43813562270441736\t-1.1183182462554362\t-0.3226956819689937\n+1.6189816606752596\t1.5416051745134067\t-0.251879139213213\t-0.8424357382512976\t0.18451869056394285\t0.9370822011089522\t0.7310003438348087\t1.361556125145331\t-0.32623805920230253\t0.055676014854776905\t6.6047216717528965\n+0.22239960855530486\t-1.4432169952253369\t-0.7563523055944354\t0.8164540110192858\t0.7504447615341785\t-0.4559469274680022\t1.1896222680291255\t-1.690616826383604\t-1.3563990488613127\t-1.2324345139149262\t-2.5402638213726094\n+-0.5444391616724643\t-0.6681717368134277\t0.007314563228903049\t-0.6129387354781626\t1.299748074755309\t-1.733095623653281\t-0.98331009912963\t0.3575077531673654\t-1.6135785028221759\t1.4707138666121289\t0.12371686928073311\n+-1.1880175973177194\t-0.5497461935354897\t-0.9400461615447682\t-0.8279323643658708\t0.1088634678336795\t0.50780959049232\t-0.8622273465104797\t1.249469742726979\t-0.07961124591739943\t-0.8897314812650339\t0.24874422979446853\n+-0.8817983894830175\t0.01863894948806016\t0.2378446219236218\t0.013548548628612411\t-1.635529399380822\t-1.044209877709317\t0.6130388816875463\t0.7362052133238238\t1.0269214393997905\t-1.4321906110589266\t-0.3078788026415402\n+-1.841188300186717\t0.36609322616730366\t-0.3317771350528086\t-0.6892179780897532\t2.0346075615049335\t-0.5507144119145928\t0.7504533303268411\t-1.3069923390808191\t0.5805733357942744\t-1.1045230926622938\t-0.19844513286689391\n+0.6901214702247076\t0.6868900661384048\t-1.566687529578391\t0.9049741214666812\t0.7788223993230673\t0.4282328705967407\t0.10887198989791452\t0.028283634823073247\t-0.5788258247909884\t-1.1994511991939312\t5.457557440881324\n+-1.7059520057381703\t0.3691639571070058\t1.8765734269621657\t-0.37690335016897475\t1.8319360818255361\t0.003017434031214063\t-0.07602346572462335\t0.003957593987599105\t-0.18501411089711395\t-2.4871515352227695\t-2.388554168522426\n+-1.7046512057609624\t-1.1362610068273629\t-2.9733154740508856\t0.03331727813886288\t-0.24888866705810792\t-0.45017643501165083\t0.13242780114877378\t0.022213928039390988\t0.31736797594106797\t-0.7524141777250374\t1.946196089279402\n+-1.2963918071501508\t0.095139443565453\t-0.42371509994342044\t-1.185983564929173\t-0.365461992676627\t-1.271023040846661\t1.5861709384232352\t0.6933906585165882\t-1.9580812342078666\t-0.13480131198999493\t0.8264174251866306\n+-1.5406160245526137\t2.0467139684821385\t-1.3969993449532836\t-1.0971719846398227\t-0.23871286931467955\t-1.429066898448291\t0.9490047765052598\t-0.019397585962465276\t0.8945977057600133\t0.7596931198502055\t8.96789675053551\n+-1.4977203810831696\t-1.193885976791938\t1.2962625863990573\t0.9522756260818906\t-1.2172541306410147\t-0.15726516737513793\t-1.5075851602643862\t0.10788413080661359\t0.7470556550991475\t0.4296764358626096\t-9.126817938253476\n+-1.415042920852526\t-0.6407599230105716\t0.7796263036636958\t-0.43812091634884287\t2.0747931679465657\t-0.34329768218246975\t-0.6166293716831945\t0.7631836460599923\t0.19291719182330652\t-0.3484589306523706\t-4.730132384531053\n+2.2986539407136757\t-0.16520955264073184\t0.46629936835718944\t0.26998723863108903\t-0.3198310471180883\t-1.1477415998765863\t1.703623988120705\t-0.7221507700557533\t1.0936866496587212\t-0.2295177532399562\t3.157726161553892\n+-0.008898663292110321\t-0.5431980084071721\t0.7530621876919789\t-1.6094388961729544\t1.943262263433996\t-1.4474361123195851\t0.13024845535270219\t0.9493608646609868\t-2.0151887171225265\t-0.07954058693411101\t-0.9240755528835013\n+0.30104946378806546\t-1.6848999616851803\t0.22239080944544862\t-0.6849217352472302\t-0.12620118371357705\t1.9902736497540905\t0.5229978045207515\t-0.016345402757487165\t-0.41581633584065\t-1.3585029367597998\t-2.2362357945556193\n+-0.5144298913687853\t-0.21606012000326133\t0.4'..b'8673364020325446\t0.6724964425333426\t-1.0909759530149077\t-0.5270034201986623\t-6.405748846172282\n+-0.30440284592936967\t0.770818433376395\t-0.23575096986827987\t-0.17778292517384178\t2.288635291333243\t-2.5289475108846857\t0.5677535540962639\t0.07355255089438108\t0.7483241867237762\t0.9146566431112833\t2.1852698329034212\n+1.2522315626273022\t-0.8847286012386693\t1.175609480746336\t0.4796962084572591\t-0.5899674340661235\t0.8621689184981023\t-1.4726571262457677\t0.6523101983645719\t-0.1516899652786653\t1.3432368831212602\t-4.239432498227812\n+-0.6594840624629351\t-0.4090657931046078\t-0.33858048238968697\t-0.3966186853856485\t-1.4582418462866669\t-0.010906592167949567\t-0.7665729756746352\t0.8421724903224964\t0.7918792014178204\t-1.317627725338646\t0.5588884412273524\n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b
diff -r 000000000000 -r 91bf3f0d7455 test-data/svc_model01.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/svc_model01.txt Sat Aug 04 12:31:24 2018 -0400
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b
diff -r 000000000000 -r 91bf3f0d7455 test-data/svc_model02.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/svc_model02.txt Sat Aug 04 12:31:24 2018 -0400
[
b'@@ -0,0 +1,305 @@\n+ccopy_reg\n+_reconstructor\n+p0\n+(csklearn.svm.classes\n+NuSVC\n+p1\n+c__builtin__\n+object\n+p2\n+Ntp3\n+Rp4\n+(dp5\n+S\'_impl\'\n+p6\n+S\'nu_svc\'\n+p7\n+sS\'kernel\'\n+p8\n+S\'rbf\'\n+p9\n+sS\'verbose\'\n+p10\n+I00\n+sS\'probability\'\n+p11\n+I00\n+sS\'classes_\'\n+p12\n+cnumpy.core.multiarray\n+_reconstruct\n+p13\n+(cnumpy\n+ndarray\n+p14\n+(I0\n+tp15\n+S\'b\'\n+p16\n+tp17\n+Rp18\n+(I1\n+(I4\n+tp19\n+cnumpy\n+dtype\n+p20\n+(S\'i8\'\n+p21\n+I0\n+I1\n+tp22\n+Rp23\n+(I3\n+S\'<\'\n+p24\n+NNNI-1\n+I-1\n+I0\n+tp25\n+bI00\n+S\'\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x02\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x03\\x00\\x00\\x00\\x00\\x00\\x00\\x00\'\n+p26\n+tp27\n+bsS\'support_\'\n+p28\n+g13\n+(g14\n+(I0\n+tp29\n+g16\n+tp30\n+Rp31\n+(I1\n+(I48\n+tp32\n+g20\n+(S\'i4\'\n+p33\n+I0\n+I1\n+tp34\n+Rp35\n+(I3\n+S\'<\'\n+p36\n+NNNI-1\n+I-1\n+I0\n+tp37\n+bI00\n+S\'\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x02\\x00\\x00\\x00\\x03\\x00\\x00\\x00\\x04\\x00\\x00\\x00\\x05\\x00\\x00\\x00\\x06\\x00\\x00\\x00\\x07\\x00\\x00\\x00\\x08\\x00\\x00\\x00\\t\\x00\\x00\\x00\\n\\x00\\x00\\x00\\x0b\\x00\\x00\\x00\\x19\\x00\\x00\\x00\\x1a\\x00\\x00\\x00\\x1b\\x00\\x00\\x00\\x1c\\x00\\x00\\x00\\x1d\\x00\\x00\\x00\\x1e\\x00\\x00\\x00\\x1f\\x00\\x00\\x00 \\x00\\x00\\x00!\\x00\\x00\\x00"\\x00\\x00\\x00#\\x00\\x00\\x00$\\x00\\x00\\x00\\x0c\\x00\\x00\\x00\\r\\x00\\x00\\x00\\x0e\\x00\\x00\\x00\\x0f\\x00\\x00\\x00\\x10\\x00\\x00\\x00\\x11\\x00\\x00\\x00\\x12\\x00\\x00\\x00\\x13\\x00\\x00\\x00\\x14\\x00\\x00\\x00\\x15\\x00\\x00\\x00\\x16\\x00\\x00\\x00\\x17\\x00\\x00\\x00\\x18\\x00\\x00\\x00%\\x00\\x00\\x00&\\x00\\x00\\x00\\\'\\x00\\x00\\x00(\\x00\\x00\\x00)\\x00\\x00\\x00*\\x00\\x00\\x00+\\x00\\x00\\x00,\\x00\\x00\\x00-\\x00\\x00\\x00.\\x00\\x00\\x00/\\x00\\x00\\x00\'\n+p38\n+tp39\n+bsS\'dual_coef_\'\n+p40\n+g13\n+(g14\n+(I0\n+tp41\n+g16\n+tp42\n+Rp43\n+(I1\n+(I3\n+I48\n+tp44\n+g20\n+(S\'f8\'\n+p45\n+I0\n+I1\n+tp46\n+Rp47\n+(I3\n+S\'<\'\n+p48\n+NNNI-1\n+I-1\n+I0\n+tp49\n+bI00\n+S\'\\xa5p\\xba\\xf57\\xfd\\xef?\\x9ak\\xb8\\xf57\\xfd\\xef?A\\xf3V\\xf0\\xca\\x02\\xf0?\\x12\\x14\\x19c\\xc4\\x02\\xf0?\\xc4\\x03\\x1c\\xc0\\xc8\\x02\\xf0?$\\xa4\\x1d\\xc0\\xc8\\x02\\xf0?\\x19`]O+\\xe4\\xef?\\xd4\\xf3;V+\\xe4\\xef?A\\xf3V\\xf0\\xca\\x02\\xf0?\\xa5p\\xba\\xf57\\xfd\\xef?\\xb0u\\xbc\\xf57\\xfd\\xef?"v\\x1c\\xc0\\xc8\\x02\\xf0?\\xb7e\\x9ee9\\xfd\\xef\\xbf\\x18\\x93\\xba\\xf57\\xfd\\xef\\xbf\\x1d\\xa0\\xba\\xf57\\xfd\\xef\\xbf\\xa5p\\xba\\xf57\\xfd\\xef\\xbf\\xa5p\\xba\\xf57\\xfd\\xef\\xbf\\xa5p\\xba\\xf57\\xfd\\xef\\xbf2N\\xba\\xf57\\xfd\\xef\\xbf\\xa5p\\xba\\xf57\\xfd\\xef\\xbf\\xa5p\\xba\\xf57\\xfd\\xef\\xbf\\x92{\\xd6\\x856\\xfd\\xef\\xbf\\xa5p\\xba\\xf57\\xfd\\xef\\xbf,A\\xba\\xf57\\xfd\\xef\\xbfKOz\\x870\\xaf\\xee\\xbf\\xc6\\xcb\\x91_\\xc6\\xb0\\xee\\xbfX\\xd2S\\xb2\\xc4\\xb0\\xee\\xbfA\\xcf\\xa0\\n\\xc3\\xb0\\xee\\xbf\\xc6\\xcb\\x91_\\xc6\\xb0\\xee\\xbf\\xed\\xde\\xf2(\\xb5\\xc0\\xee\\xbf\\x91b\\x9f\\n\\xc3\\xb0\\xee\\xbf\\x8b\\xf5F\\x89+\\xaf\\xee\\xbf\\x19\\xdb\\xf3\\\'\\xb5\\xc0\\xee\\xbf#\\x8c\\x9d\\n\\xc3\\xb0\\xee\\xbf\\xb2\\xc9}]\\xc6\\xb0\\xee\\xbf\\xd4\\x07\\xf1(\\xb5\\xc0\\xee\\xbf\\x19\\xdb\\xf3\\\'\\xb5\\xc0\\xee\\xbf\\xa1Zv\\xa1\\x87\\xae\\xf0\\xbf1\\xba\\xf5Y\\x87\\xae\\xf0\\xbf\\xed\\xdc\\x98^|\\xae\\xf0\\xbf\\xa1Zv\\xa1\\x87\\xae\\xf0\\xbf\\x81\\x1fN\\x07\\x82\\xb6\\xf0\\xbf\\xb9\\xb3\\\'\\x06\\x82\\xb6\\xf0\\xbfEI\\xd8F|\\xae\\xf0\\xbf\\xf5Ivl\\x87\\xae\\xf0\\xbf\\xa7\\xbdz\\xa1\\x87\\xae\\xf0\\xbf)\\x88A\\xa0\\x87\\xae\\xf0\\xbf&\\x16Z\\x07\\x82\\xb6\\xf0\\xbf^rU\\xfb\\xf5\\xa2\\xf0?\\xfa\\x89d\\x8c\\x8d\\xa9\\xf0?\\xcd\\x8bb\\x0e\\xf6\\xa2\\xf0?dFh\\x8c\\x8d\\xa9\\xf0?\\x88\\xe4d\\x0e\\xf6\\xa2\\xf0?;\\x88\\xcd\\x84\\xee\\xa2\\xf0?\\xbf\\xbb*V\\xab\\x91\\xf0?I\\xcdW7\\xa7\\x91\\xf0?dFh\\x8c\\x8d\\xa9\\xf0?\\xbd\\xd9\\xb8q\\xee\\xa2\\xf0?\\x17Hh\\x8c\\x8d\\xa9\\xf0?\\x19\\xbd\\xec\\x02\\x01\\xa3\\xf0?\\x15\\x0eSC\\x1d\\xa1\\xf0?\\xa1^\\xaf\\xd5\\x19\\xa9\\xf0?\\xcc\\x9bfD\\x1d\\xa1\\xf0?\\x96\\x16\\xaf\\xd5\\x19\\xa9\\xf0?\\x08\\x0eSC\\x1d\\xa1\\xf0?\\x15\\x0eSC\\x1d\\xa1\\xf0?@M\\x8d\\xc5\\x1b\\xa1\\xf0?%l\\xaf\\xd5\\x19\\xa9\\xf0?m!\\x9bB\\x1d\\xa1\\xf0?C\\x03\\xe9\\xc5\\x1b\\xa1\\xf0?\\xfcY\\x9bB\\x1d\\xa1\\xf0?\\xecV\\xaf\\xd5\\x19\\xa9\\xf0?\\xb6&\\xe8\\xc1\\xf5\\xb1\\xee\\xbfj1\\xa4\\xbe\\x8b\\xb3\\xee\\xbf\\x9ev?\\x11\\x8a\\xb3\\xee\\xbfF2fi\\x88\\xb3\\xee\\xbfj1\\xa4\\xbe\\x8b\\xb3\\xee\\xbf\\x92\\x15I\\xf8{\\xc3\\xee\\xbfu\\xc5di\\x88\\xb3\\xee\\xbf\\x08eA\\xc3\\xf0\\xb1\\xee\\xbf\\xb8\\xfaI\\xf7{\\xc3\\xee\\xbf\\xdc\\xeebi\\x88\\xb3\\xee\\xbfN\\xff\\x8f\\xbc\\x8b\\xb3\\xee\\xbfN>G\\xf8{\\xc3\\xee\\xbf\\xb8\\xfaI\\xf7{\\xc3\\xee\\xbf\\xc3\\x7f[\\xfe\\xe9\\xaf\\xf0\\xbfn\\xf0\\xd4\\xb6\\xe9\\xaf\\xf0\\xbf\\xaf\\xca\\x8e\\xba\\xde\\xaf\\xf0\\xbf\\xc3\\x7f[\\xfe\\xe9\\xaf\\xf0\\xbf\\xad\\xfb\\xac\\r\\xe5\\xb7\\xf0\\xbfww\\x86\\x0c\\xe5\\xb7\\xf0\\xbfx>\\xcc\\xa2\\xde\\xaf\\xf0\\x'..b'0\\x00\\x00\\x00\\x003@\\x00\\x00\\x00\\x00\\x00\\x80[\\xc0\\x00\\x00\\x00\\x00\\x00\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b
diff -r 000000000000 -r 91bf3f0d7455 test-data/svc_model03.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/svc_model03.txt Sat Aug 04 12:31:24 2018 -0400
[
@@ -0,0 +1,163 @@
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\ No newline at end of file
b
diff -r 000000000000 -r 91bf3f0d7455 test-data/svc_prediction_result01.tabular
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/svc_prediction_result01.tabular Sat Aug 04 12:31:24 2018 -0400
b
@@ -0,0 +1,49 @@
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diff -r 000000000000 -r 91bf3f0d7455 test-data/svc_prediction_result02.tabular
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
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diff -r 000000000000 -r 91bf3f0d7455 test-data/svc_prediction_result03.tabular
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
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diff -r 000000000000 -r 91bf3f0d7455 test-data/swiss_r.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
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diff -r 000000000000 -r 91bf3f0d7455 test-data/test.tabular
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/test.tabular Sat Aug 04 12:31:24 2018 -0400
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diff -r 000000000000 -r 91bf3f0d7455 test-data/test2.tabular
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/test2.tabular Sat Aug 04 12:31:24 2018 -0400
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diff -r 000000000000 -r 91bf3f0d7455 test-data/test3.tabular
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/test3.tabular Sat Aug 04 12:31:24 2018 -0400
b
@@ -0,0 +1,51 @@
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diff -r 000000000000 -r 91bf3f0d7455 test-data/test_set.tabular
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
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diff -r 000000000000 -r 91bf3f0d7455 test-data/train.tabular
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
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diff -r 000000000000 -r 91bf3f0d7455 test-data/train_set.tabular
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
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b
diff -r 000000000000 -r 91bf3f0d7455 test-data/vectorizer_result01.mtx
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/vectorizer_result01.mtx Sat Aug 04 12:31:24 2018 -0400
b
b'@@ -0,0 +1,3788 @@\n+%%MatrixMarket matrix coordinate real general\n+%\n+2 1048577 3785\n+1 1450 0.01431896616372403\n+1 1565 0.01370375121911989\n+1 1889 0.02282816271614068\n+1 1961 0.01205944593190492\n+1 2759 0.01929331884718118\n+1 2860 0.01370375121911989\n+1 4053 0.01894755120823075\n+1 4119 0.01370375121911989\n+1 4727 0.01205944593190492\n+1 6201 0.02791992442867134\n+1 6714 0.02282816271614068\n+1 7170 0.0127117721548238\n+1 8116 0.02282816271614068\n+1 9508 0.01370375121911989\n+1 9758 0.01431896616372403\n+1 9870 0.01431896616372403\n+1 10078 0.05787995654154355\n+1 10492 0.02791992442867134\n+1 10495 0.02852128038707927\n+1 12091 0.05210576582263455\n+1 13013 0.01431896616372403\n+1 13234 0.009507093462359758\n+1 13829 0.0127117721548238\n+1 14120 0.01861328295244756\n+1 14517 0.02282816271614068\n+1 15083 0.01431896616372403\n+1 16177 0.01205944593190492\n+1 16761 0.02282816271614068\n+1 17410 0.01370375121911989\n+1 17436 0.01431896616372403\n+1 18255 0.02282816271614068\n+1 18283 0.01205944593190492\n+1 19133 0.02863793232744806\n+1 19449 0.02282816271614068\n+1 19510 0.01431896616372403\n+1 20975 0.07579020483292298\n+1 21531 0.01370375121911989\n+1 22178 0.004736887802057686\n+1 22283 0.01663440851737127\n+1 22706 0.02740750243823978\n+1 22715 0.01370375121911989\n+1 23698 0.01205944593190492\n+1 24190 0.01861328295244756\n+1 24309 0.02282816271614068\n+1 24541 0.01370375121911989\n+1 25019 0.01205944593190492\n+1 25500 0.01861328295244756\n+1 26513 0.0127117721548238\n+1 26569 0.01929331884718118\n+1 27192 0.01431896616372403\n+1 27389 0.02791992442867134\n+1 27912 0.0127117721548238\n+1 28011 0.02282816271614068\n+1 29313 0.02495161277605691\n+1 29735 0.03858663769436237\n+1 30453 0.03326881703474255\n+1 31110 0.02863793232744806\n+1 31588 0.02740750243823978\n+1 31963 0.01370375121911989\n+1 31975 0.01929331884718118\n+1 32413 0.01370375121911989\n+1 33140 0.02791992442867134\n+1 33433 0.01929331884718118\n+1 33979 0.02495161277605691\n+1 34275 0.01431896616372403\n+1 35828 0.0127117721548238\n+1 35928 0.01663440851737127\n+1 36463 0.01929331884718118\n+1 37218 0.01431896616372403\n+1 37731 0.01929331884718118\n+1 38358 0.01370375121911989\n+1 39516 0.02147844924558604\n+1 39526 0.01370375121911989\n+1 39653 0.02282816271614068\n+1 39705 0.01205944593190492\n+1 39762 0.02542354430964759\n+1 40007 0.006403636341921261\n+1 40920 0.004736887802057686\n+1 40975 0.0127117721548238\n+1 41381 0.01370375121911989\n+1 41399 0.01861328295244756\n+1 41645 0.01929331884718118\n+1 41800 0.02191386749905983\n+1 42763 0.01370375121911989\n+1 42946 0.01370375121911989\n+1 43077 0.02282816271614068\n+1 43474 0.01431896616372403\n+1 43781 0.01901418692471952\n+1 43937 0.02282816271614068\n+1 44721 0.02282816271614068\n+1 45315 0.01431896616372403\n+1 45359 0.02282816271614068\n+1 45447 0.02495161277605691\n+1 45548 0.01929331884718118\n+1 46264 0.01205944593190492\n+1 46627 0.006403636341921261\n+1 47125 0.01861328295244756\n+1 48545 0.0127117721548238\n+1 49094 0.01861328295244756\n+1 49619 0.01370375121911989\n+1 50271 0.01431896616372403\n+1 50742 0.04026354631749034\n+1 51430 0.01370375121911989\n+1 52454 0.03177943038705949\n+1 52548 0.02282816271614068\n+1 52666 0.01370375121911989\n+1 52833 0.003201818170960631\n+1 54362 0.01431896616372403\n+1 54371 0.01861328295244756\n+1 56154 0.02411889186380985\n+1 57027 0.01431896616372403\n+1 57543 0.02282816271614068\n+1 57633 0.02542354430964759\n+1 58575 0.02863793232744806\n+1 58806 0.02282816271614068\n+1 58964 0.009473775604115373\n+1 59388 0.02282816271614068\n+1 59411 0.01370375121911989\n+1 59679 0.0127117721548238\n+1 60697 0.01205944593190492\n+1 61808 0.01861328295244756\n+1 63572 0.02740750243823978\n+1 64006 0.01426064019353964\n+1 64561 0.01205944593190492\n+1 65197 0.01861328295244756\n+1 65817 0.05720297469670708\n+1 66479 0.02740750243823978\n+1 66876 0.01861328295244756\n+1 67128 0.03425937804779972\n+1 68440 0.004736887802057686\n+1 68573 0.004753546731179879\n+1 70299 0.01370375121911989\n+1 70576 0.02791992442867134\n+1 71207 0.01929331884718118\n+1 71899 0.01861328295244756\n+1 72251 0.013'..b'674637\n+2 973333 0.01450010501959994\n+2 974699 0.0224596916827213\n+2 974851 0.01367466109027461\n+2 975218 0.01288938340939221\n+2 975811 0.0224596916827213\n+2 977200 0.0276941383527154\n+2 978046 0.01846275890181026\n+2 978257 0.05469864436109843\n+2 978285 0.02084171182520092\n+2 978287 0.02900021003919988\n+2 978511 0.0224596916827213\n+2 978773 0.02720735217809617\n+2 979446 0.02084171182520092\n+2 979478 0.0224596916827213\n+2 979904 0.01933407511408831\n+2 980261 0.01846275890181026\n+2 980319 0.01360367608904809\n+2 981883 0.01450010501959994\n+2 981921 0.0224596916827213\n+2 982012 0.01360367608904809\n+2 983673 0.01288938340939221\n+2 983781 0.01450010501959994\n+2 984700 0.01846275890181026\n+2 985549 0.02734932218054922\n+2 986324 0.0224596916827213\n+2 986791 0.0224596916827213\n+2 989163 0.02577876681878442\n+2 989433 0.027349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b
diff -r 000000000000 -r 91bf3f0d7455 test-data/vectorizer_result02.mtx
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/vectorizer_result02.mtx Sat Aug 04 12:31:24 2018 -0400
b
b'@@ -0,0 +1,7346 @@\n+%%MatrixMarket matrix coordinate real general\n+%\n+4 1048577 7343\n+1 1450 0.0152274037172392\n+1 1961 0.01275776096436371\n+1 4053 0.01582710983069052\n+1 4357 0.01446327540314412\n+1 4379 0.02292554618019654\n+1 4727 0.01275776096436371\n+1 6201 0.02859906492378073\n+1 7170 0.01341424712825137\n+1 8116 0.02292554618019654\n+1 10078 0.05679044019028674\n+1 10495 0.03256516109420249\n+1 12091 0.05275703276896841\n+1 12155 0.01906604328252049\n+1 13013 0.01517172746603997\n+1 13181 0.0152274037172392\n+1 13234 0.0108550536980675\n+1 13280 0.0152274037172392\n+1 14120 0.01906604328252049\n+1 14527 0.0152274037172392\n+1 15083 0.0152274037172392\n+1 15241 0.01446327540314412\n+1 15283 0.01895709357182213\n+1 16177 0.01275776096436371\n+1 17436 0.0152274037172392\n+1 19449 0.02255274822664651\n+1 20975 0.05803273604586524\n+1 21531 0.0144155416907593\n+1 22178 0.00527570327689684\n+1 22283 0.01674245171469339\n+1 22706 0.02892655080628824\n+1 22715 0.0144155416907593\n+1 23698 0.01275776096436371\n+1 24309 0.02255274822664651\n+1 24541 0.0144155416907593\n+1 25019 0.01275776096436371\n+1 26513 0.01341424712825137\n+1 26569 0.01893014673009558\n+1 27172 0.01446327540314412\n+1 27389 0.02859906492378073\n+1 29313 0.02511367757204008\n+1 29735 0.03786029346019116\n+1 30445 0.0152274037172392\n+1 30453 0.05022735514408017\n+1 30833 0.01341424712825137\n+1 31588 0.0288310833815186\n+1 31963 0.0144155416907593\n+1 33113 0.01517172746603997\n+1 33433 0.01893014673009558\n+1 33979 0.02511367757204008\n+1 34869 0.01895709357182213\n+1 35828 0.01341424712825137\n+1 35928 0.01674245171469339\n+1 36463 0.01893014673009558\n+1 37731 0.01893014673009558\n+1 37859 0.01275776096436371\n+1 38203 0.0144155416907593\n+1 39526 0.01446327540314412\n+1 39705 0.01275776096436371\n+1 39762 0.02682849425650273\n+1 40007 0.01412754739202977\n+1 40920 0.00527570327689684\n+1 40975 0.01341424712825137\n+1 41385 0.01906604328252049\n+1 41559 0.0288310833815186\n+1 41645 0.01893014673009558\n+1 41800 0.03886244041341189\n+1 43077 0.02255274822664651\n+1 43474 0.0152274037172392\n+1 43781 0.01628258054710124\n+1 43937 0.02292554618019654\n+1 44401 0.01895709357182213\n+1 45359 0.02255274822664651\n+1 45447 0.02511367757204008\n+1 45548 0.01893014673009558\n+1 46264 0.01275776096436371\n+1 46627 0.007063773696014885\n+1 48545 0.01341424712825137\n+1 50045 0.02255274822664651\n+1 50271 0.01517172746603997\n+1 50742 0.05803273604586524\n+1 50871 0.01517172746603997\n+1 51188 0.01446327540314412\n+1 52548 0.02255274822664651\n+1 52666 0.01446327540314412\n+1 52833 0.003531886848007443\n+1 53247 0.01913664144654556\n+1 54371 0.01895709357182213\n+1 55601 0.0144155416907593\n+1 56154 0.02551552192872741\n+1 57027 0.01517172746603997\n+1 57543 0.02292554618019654\n+1 57633 0.02682849425650273\n+1 58055 0.03786029346019116\n+1 58575 0.03045480743447839\n+1 58964 0.02110281310758736\n+1 59388 0.02255274822664651\n+1 59679 0.01341424712825137\n+1 60697 0.01275776096436371\n+1 61808 0.01895709357182213\n+1 63572 0.0288310833815186\n+1 64006 0.01628258054710124\n+1 64516 0.02169491310471618\n+1 65817 0.0402427413847541\n+1 66000 0.02859906492378073\n+1 66876 0.01906604328252049\n+1 68440 0.00527570327689684\n+1 68573 0.005427526849033748\n+1 68652 0.03034345493207994\n+1 69297 0.01893014673009558\n+1 69935 0.01446327540314412\n+1 70576 0.0284356403577332\n+1 70624 0.01446327540314412\n+1 71207 0.01893014673009558\n+1 71899 0.01906604328252049\n+1 72251 0.01446327540314412\n+1 72260 0.0152274037172392\n+1 72351 0.02292554618019654\n+1 73005 0.01275776096436371\n+1 73634 0.0144155416907593\n+1 73778 0.0144155416907593\n+1 75637 0.01906604328252049\n+1 75685 0.01674245171469339\n+1 76187 0.01341424712825137\n+1 76858 0.01446327540314412\n+1 77048 0.0144155416907593\n+1 78707 0.01895709357182213\n+1 79120 0.02255274822664651\n+1 79445 0.01628258054710124\n+1 79790 0.01895709357182213\n+1 80087 0.02110281310758736\n+1 80609 0.01341424712825137\n+1 80905 0.005427526849033748\n+1 82201 0.02292554618019654\n+1 82271 0.0144155416907593\n+1 84117 0.01895709357182213\n+1 84121 0.01893014673009558\n'..b'3096885777\n+4 967164 0.007264789390163445\n+4 967313 0.003055503872267962\n+4 967983 0.02245327618246397\n+4 968163 0.02288809645328665\n+4 968483 0.02876453510426408\n+4 969841 0.02179436817049034\n+4 970265 0.01525873096885777\n+4 971015 0.01917635673617606\n+4 972013 0.01425830829592147\n+4 972178 0.02876453510426408\n+4 972485 0.01695356088516536\n+4 973099 0.01586088310577529\n+4 973333 0.01586088310577529\n+4 974072 0.01525873096885777\n+4 974851 0.01425830829592147\n+4 975811 0.02245327618246397\n+4 976275 0.01525873096885777\n+4 977200 0.02876453510426408\n+4 977905 0.01360986045640215\n+4 978257 0.05703323318368587\n+4 978511 0.02245327618246397\n+4 978773 0.03051746193771554\n+4 979478 0.02245327618246397\n+4 980261 0.01917635673617606\n+4 980319 0.01525873096885777\n+4 981883 0.01586088310577529\n+4 982012 0.01525873096885777\n+4 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b
diff -r 000000000000 -r 91bf3f0d7455 test-data/vectorizer_result03.mtx
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/vectorizer_result03.mtx Sat Aug 04 12:31:24 2018 -0400
b
b'@@ -0,0 +1,9697 @@\n+%%MatrixMarket matrix coordinate real general\n+%\n+4 1048577 9694\n+1 71 0.01361331290463183\n+1 1450 0.01361331290463183\n+1 1961 0.01140545001599714\n+1 3747 0.01361331290463183\n+1 3795 0.02049545933475693\n+1 4053 0.01414945072069242\n+1 4357 0.01293018149022739\n+1 4379 0.02049545933475693\n+1 4727 0.01140545001599714\n+1 5795 0.01704506020053583\n+1 6201 0.02556759030080374\n+1 7170 0.01199234924928183\n+1 8116 0.02049545933475693\n+1 8523 0.01293018149022739\n+1 8531 0.01704506020053583\n+1 9300 0.02041996935694774\n+1 10078 0.05077070567367346\n+1 10495 0.02911328391873078\n+1 12091 0.0471648357356414\n+1 12155 0.01704506020053583\n+1 13013 0.01356353828493919\n+1 13181 0.01361331290463183\n+1 13234 0.009704427972910257\n+1 13280 0.01361331290463183\n+1 14120 0.01704506020053583\n+1 14527 0.01361331290463183\n+1 14873 0.01293018149022739\n+1 15083 0.01361331290463183\n+1 15241 0.01293018149022739\n+1 15283 0.01694765905913661\n+1 16177 0.01140545001599714\n+1 17436 0.01361331290463183\n+1 19449 0.02016217762198948\n+1 20413 0.01361331290463183\n+1 20975 0.05188131930920554\n+1 21084 0.01293018149022739\n+1 21531 0.01288750750752746\n+1 22178 0.004716483573564139\n+1 22283 0.01496776720543493\n+1 22554 0.01939527223534108\n+1 22570 0.01293018149022739\n+1 22706 0.02586036298045478\n+1 22715 0.01288750750752746\n+1 23188 0.01361331290463183\n+1 23698 0.01140545001599714\n+1 24309 0.02016217762198948\n+1 24510 0.01293018149022739\n+1 24541 0.01288750750752746\n+1 25019 0.01140545001599714\n+1 26513 0.01199234924928183\n+1 26569 0.01692356855789115\n+1 26721 0.01293018149022739\n+1 27172 0.01293018149022739\n+1 27389 0.02556759030080374\n+1 28621 0.01293018149022739\n+1 29313 0.0224516508081524\n+1 29735 0.03384713711578231\n+1 30007 0.02556759030080374\n+1 30445 0.01361331290463183\n+1 30453 0.04490330161630479\n+1 30833 0.01199234924928183\n+1 31159 0.01704506020053583\n+1 31588 0.02577501501505492\n+1 31713 0.01704506020053583\n+1 31931 0.02049545933475693\n+1 31963 0.01288750750752746\n+1 33113 0.01356353828493919\n+1 33433 0.01692356855789115\n+1 33979 0.0224516508081524\n+1 34869 0.01293018149022739\n+1 35056 0.01704506020053583\n+1 35828 0.01199234924928183\n+1 35928 0.01496776720543493\n+1 36463 0.01692356855789115\n+1 36581 0.01361331290463183\n+1 37021 0.01293018149022739\n+1 37256 0.01361331290463183\n+1 37731 0.01692356855789115\n+1 37831 0.01361331290463183\n+1 37859 0.01140545001599714\n+1 38203 0.01288750750752746\n+1 38978 0.02049545933475693\n+1 39526 0.01293018149022739\n+1 39705 0.01140545001599714\n+1 39762 0.02398469849856365\n+1 40007 0.01263004034003412\n+1 40920 0.004716483573564139\n+1 40975 0.01199234924928183\n+1 41367 0.01704506020053583\n+1 41385 0.01704506020053583\n+1 41559 0.02577501501505492\n+1 41645 0.01692356855789115\n+1 41800 0.03474305741210783\n+1 42042 0.02049545933475693\n+1 42057 0.01361331290463183\n+1 42103 0.01293018149022739\n+1 43077 0.02016217762198948\n+1 43474 0.01361331290463183\n+1 43781 0.01455664195936539\n+1 43937 0.02049545933475693\n+1 44401 0.01694765905913661\n+1 45359 0.02016217762198948\n+1 45447 0.0224516508081524\n+1 45548 0.01692356855789115\n+1 45746 0.01704506020053583\n+1 46264 0.01140545001599714\n+1 46627 0.006315020170017061\n+1 48545 0.01199234924928183\n+1 50045 0.02016217762198948\n+1 50271 0.01356353828493919\n+1 50742 0.05188131930920554\n+1 50871 0.01356353828493919\n+1 51188 0.01293018149022739\n+1 51486 0.02049545933475693\n+1 52094 0.01704506020053583\n+1 52548 0.02016217762198948\n+1 52666 0.01293018149022739\n+1 52833 0.003157510085008531\n+1 53247 0.0171081750239957\n+1 53497 0.01293018149022739\n+1 54371 0.01694765905913661\n+1 55601 0.01288750750752746\n+1 55999 0.02049545933475693\n+1 56154 0.02281090003199427\n+1 57027 0.01356353828493919\n+1 57543 0.02049545933475693\n+1 57633 0.02398469849856365\n+1 58055 0.03384713711578231\n+1 58277 0.01704506020053583\n+1 58575 0.02722662580926366\n+1 58845 0.01361331290463183\n+1 58964 0.01886593429425656\n+1 59388 0.02016217762198948\n+1 59679 0.01199234924928183\n+1 60697 0.01140545001599714\n+1 61549 0.013613'..b' 0.02434907336554575\n+4 989433 0.02550919576861992\n+4 990804 0.01217453668277288\n+4 991468 0.006824756966025142\n+4 991475 0.02837632374130612\n+4 991935 0.01648847232539554\n+4 991985 0.01715397885778408\n+4 992490 0.01418816187065306\n+4 993053 0.03412378483012571\n+4 993510 0.02008530766406564\n+4 994106 0.02729902786410057\n+4 994950 0.01217453668277288\n+4 995605 0.01275459788430996\n+4 995653 0.01648847232539554\n+4 995751 0.02008530766406564\n+4 996536 0.01217453668277288\n+4 997796 0.02729902786410057\n+4 997962 0.02008530766406564\n+4 997977 0.02008530766406564\n+4 998855 0.01418816187065306\n+4 999264 0.01364951393205028\n+4 1000099 0.01364951393205028\n+4 1000335 0.01418816187065306\n+4 1000999 0.01715397885778408\n+4 1001147 0.02008530766406564\n+4 1001734 0.01715397885778408\n+4 1002607 0.01715397885778408\n+4 1002835 0.0200853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b
diff -r 000000000000 -r 91bf3f0d7455 test-data/vectorizer_result04.mtx
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/vectorizer_result04.mtx Sat Aug 04 12:31:24 2018 -0400
b
b'@@ -0,0 +1,9697 @@\n+%%MatrixMarket matrix coordinate real general\n+%\n+4 1048577 9694\n+1 71 0.01361331290463183\n+1 1450 0.01361331290463183\n+1 1961 0.01140545001599714\n+1 3747 0.01361331290463183\n+1 3795 0.02049545933475693\n+1 4053 0.01414945072069242\n+1 4357 0.01293018149022739\n+1 4379 0.02049545933475693\n+1 4727 0.01140545001599714\n+1 5795 0.01704506020053583\n+1 6201 0.02556759030080374\n+1 7170 0.01199234924928183\n+1 8116 0.02049545933475693\n+1 8523 0.01293018149022739\n+1 8531 0.01704506020053583\n+1 9300 0.02041996935694774\n+1 10078 0.05077070567367346\n+1 10495 0.02911328391873078\n+1 12091 0.0471648357356414\n+1 12155 0.01704506020053583\n+1 13013 0.01356353828493919\n+1 13181 0.01361331290463183\n+1 13234 0.009704427972910257\n+1 13280 0.01361331290463183\n+1 14120 0.01704506020053583\n+1 14527 0.01361331290463183\n+1 14873 0.01293018149022739\n+1 15083 0.01361331290463183\n+1 15241 0.01293018149022739\n+1 15283 0.01694765905913661\n+1 16177 0.01140545001599714\n+1 17436 0.01361331290463183\n+1 19449 0.02016217762198948\n+1 20413 0.01361331290463183\n+1 20975 0.05188131930920554\n+1 21084 0.01293018149022739\n+1 21531 0.01288750750752746\n+1 22178 0.004716483573564139\n+1 22283 0.01496776720543493\n+1 22554 0.01939527223534108\n+1 22570 0.01293018149022739\n+1 22706 0.02586036298045478\n+1 22715 0.01288750750752746\n+1 23188 0.01361331290463183\n+1 23698 0.01140545001599714\n+1 24309 0.02016217762198948\n+1 24510 0.01293018149022739\n+1 24541 0.01288750750752746\n+1 25019 0.01140545001599714\n+1 26513 0.01199234924928183\n+1 26569 0.01692356855789115\n+1 26721 0.01293018149022739\n+1 27172 0.01293018149022739\n+1 27389 0.02556759030080374\n+1 28621 0.01293018149022739\n+1 29313 0.0224516508081524\n+1 29735 0.03384713711578231\n+1 30007 0.02556759030080374\n+1 30445 0.01361331290463183\n+1 30453 0.04490330161630479\n+1 30833 0.01199234924928183\n+1 31159 0.01704506020053583\n+1 31588 0.02577501501505492\n+1 31713 0.01704506020053583\n+1 31931 0.02049545933475693\n+1 31963 0.01288750750752746\n+1 33113 0.01356353828493919\n+1 33433 0.01692356855789115\n+1 33979 0.0224516508081524\n+1 34869 0.01293018149022739\n+1 35056 0.01704506020053583\n+1 35828 0.01199234924928183\n+1 35928 0.01496776720543493\n+1 36463 0.01692356855789115\n+1 36581 0.01361331290463183\n+1 37021 0.01293018149022739\n+1 37256 0.01361331290463183\n+1 37731 0.01692356855789115\n+1 37831 0.01361331290463183\n+1 37859 0.01140545001599714\n+1 38203 0.01288750750752746\n+1 38978 0.02049545933475693\n+1 39526 0.01293018149022739\n+1 39705 0.01140545001599714\n+1 39762 0.02398469849856365\n+1 40007 0.01263004034003412\n+1 40920 0.004716483573564139\n+1 40975 0.01199234924928183\n+1 41367 0.01704506020053583\n+1 41385 0.01704506020053583\n+1 41559 0.02577501501505492\n+1 41645 0.01692356855789115\n+1 41800 0.03474305741210783\n+1 42042 0.02049545933475693\n+1 42057 0.01361331290463183\n+1 42103 0.01293018149022739\n+1 43077 0.02016217762198948\n+1 43474 0.01361331290463183\n+1 43781 0.01455664195936539\n+1 43937 0.02049545933475693\n+1 44401 0.01694765905913661\n+1 45359 0.02016217762198948\n+1 45447 0.0224516508081524\n+1 45548 0.01692356855789115\n+1 45746 0.01704506020053583\n+1 46264 0.01140545001599714\n+1 46627 0.006315020170017061\n+1 48545 0.01199234924928183\n+1 50045 0.02016217762198948\n+1 50271 0.01356353828493919\n+1 50742 0.05188131930920554\n+1 50871 0.01356353828493919\n+1 51188 0.01293018149022739\n+1 51486 0.02049545933475693\n+1 52094 0.01704506020053583\n+1 52548 0.02016217762198948\n+1 52666 0.01293018149022739\n+1 52833 0.003157510085008531\n+1 53247 0.0171081750239957\n+1 53497 0.01293018149022739\n+1 54371 0.01694765905913661\n+1 55601 0.01288750750752746\n+1 55999 0.02049545933475693\n+1 56154 0.02281090003199427\n+1 57027 0.01356353828493919\n+1 57543 0.02049545933475693\n+1 57633 0.02398469849856365\n+1 58055 0.03384713711578231\n+1 58277 0.01704506020053583\n+1 58575 0.02722662580926366\n+1 58845 0.01361331290463183\n+1 58964 0.01886593429425656\n+1 59388 0.02016217762198948\n+1 59679 0.01199234924928183\n+1 60697 0.01140545001599714\n+1 61549 0.013613'..b' 0.02434907336554575\n+4 989433 0.02550919576861992\n+4 990804 0.01217453668277288\n+4 991468 0.006824756966025142\n+4 991475 0.02837632374130612\n+4 991935 0.01648847232539554\n+4 991985 0.01715397885778408\n+4 992490 0.01418816187065306\n+4 993053 0.03412378483012571\n+4 993510 0.02008530766406564\n+4 994106 0.02729902786410057\n+4 994950 0.01217453668277288\n+4 995605 0.01275459788430996\n+4 995653 0.01648847232539554\n+4 995751 0.02008530766406564\n+4 996536 0.01217453668277288\n+4 997796 0.02729902786410057\n+4 997962 0.02008530766406564\n+4 997977 0.02008530766406564\n+4 998855 0.01418816187065306\n+4 999264 0.01364951393205028\n+4 1000099 0.01364951393205028\n+4 1000335 0.01418816187065306\n+4 1000999 0.01715397885778408\n+4 1001147 0.02008530766406564\n+4 1001734 0.01715397885778408\n+4 1002607 0.01715397885778408\n+4 1002835 0.0200853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b
diff -r 000000000000 -r 91bf3f0d7455 test-data/y.tabular
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/y.tabular Sat Aug 04 12:31:24 2018 -0400
b
@@ -0,0 +1,39 @@
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b
diff -r 000000000000 -r 91bf3f0d7455 test-data/zero_one_loss.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/zero_one_loss.txt Sat Aug 04 12:31:24 2018 -0400
b
@@ -0,0 +1,2 @@
+zero_one_loss : 
+0.15384615384615385